Exploring investment requirements for energy efficiency upgrades in the private rental sector

K en, Janez; Kenny, Eoin; O'Toole, Cono ; Shiel, E a; Slaymake , Rachel
Resea ch Repo
Explo ing in es men equi emen s o ene gy e iciency
upg ades in he p i a e en al sec o
Resea ch Se ies, No. 205
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The Economic and Social Resea ch Ins i u e (ESRI), Dublin
Sugges ed Ci a ion: K en, Janez; Kenny, Eoin; O'Toole, Cono ; Shiel, E a; Slaymake , Rachel (2025) :
Explo ing in es men equi emen s o ene gy e iciency upg ades in he p i a e en al sec o ,
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Explo ing in es men
equi emen s o ene gy
e iciency upg ades in he
p i a e en al sec o
JANEZ KREN, EOIN KENNY, CONOR O’TOOLE, EVA SHIEL AND
RACHEL SLAYMAKER
ESRI
RESEARCH SERIES
Numbe 205, Feb ua y 2025
EXPLORING INVESTMENT REQUIREMENTS FOR
ENERGY EFFICIENCY UPGRADES IN THE PRIVATE
RENTAL SECTOR
Janez K en
Eoin Kenny
Cono O’Toole
E a Shiel
Rachel Slaymake
Feb ua y 2025
ESRI RESEARCH SERIES
NUMBER 205
A ailable o download om ww.es i.ie
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THE AUTHORS
Cono O’Toole is an Associa e Resea ch P o esso a he ESRI and an Adjunc
P o esso a T ini y College Dublin (TCD). Janez K en and Rachel Slaymake a e
Resea ch O ice s a he ESRI and hold Adjunc Assis an P o esso posi ions a TCD.
Eoin Kenny and E a Shiel we e p e iously Resea ch Assis an s a he ESRI.
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Table o con en s| i
TABLE OF CONTENTS
LIST OF ABBREVIATIONS ..........................................................................................................................
EXECUTIVE SUMMARY ............................................................................................................................ i
CHAPTER 1 INTRODUCTION ..................................................................................................................... 1
CHAPTER 2 BACKGROUND AND CONTEXT ............................................................................................... 5
2.1 The I ish housing ma ke ..................................................................................................... 5
2.2 In e na ional li e a u e ........................................................................................................ 9
2.3 Summa y o ele an I ish esea ch .................................................................................. 12
CHAPTER 3 PROFILING THE ENERGY EFFICIENCY OF THE PRIVATE RENTAL SECTOR ............................ 14
3.1 In oduc ion ....................................................................................................................... 14
3.2 RTB egis a ions da a ....................................................................................................... 14
3.3 SEAI BER da abase ............................................................................................................. 19
3.4 Census econcilia ion and compa a i e analysis ............................................................... 25
CHAPTER 4 UNDERSTANDING INVESTMENT EXPENDITURE NEEDS ...................................................... 32
4.1 In oduc ion ....................................................................................................................... 32
4.2 Cos o e iciency upg ade da a ......................................................................................... 32
4.3 Es ima ion o upg ade cos pe dwelling ........................................................................... 37
4.4 Towa ds an agg ega e cos o PRS dwelling upg ades ...................................................... 41
CHAPTER 5 INVESTMENT BARRIERS AND THE LANDLORD STRUCTURE ................................................ 46
5.1 Da a and demog aphic p o ile o household landlo ds .................................................... 47
5.2 Simula ing hypo he ical inancing gaps ............................................................................. 57
CHAPTER 6 CONCLUDING REMARKS...................................................................................................... 68
6.1 Findings on in es men needs ........................................................................................... 68
6.2 Findings on he landlo d s uc u e and inancial capaci y o in es ................................. 69
REFERENCES .............................................................................................................................. 72
APPENDIX A ADDITIONAL RESULTS ........................................................................................................ 75
APPENDIX B PROTECTED HERITAGE BUILDINGS .................................................................................... 78
APPENDIX C REGRESSION RESULTS FOR MISSING RTB BER RATINGS ................................................... 80
ii|In es men equi emen s o ene gy e iciency upg ades in he en al sec o
LIST OF TABLES
Table 3.1 RTB- egis e ed p ope ies ac oss egis a ion ype ............................................. 15
Table 3.2 Cha ac e is ics o p ope ies wi h BER compa ed o wi hou BER ....................... 26
Table 3.3 Cha ac e is ics o p ope ies wi h BER compa ed o wi hou BER ....................... 26
Table 4.1 Numbe o obse a ions by da ase ..................................................................... 34
Table 4.2 Example using RTB da a and quad a ic i upg ade cos s .................................... 43
Table 4.3 Es ima ed agg ega e cos s o upg ade o B* o all G–C1 dwellings (€mn) ......... 44
Table 5.1 Summa y o landlo d annual household income da a – su ey yea 2020 .......... 52
Table 5.2 Summa y o landlo d annual en al income da a – su ey yea s 2018/2020 ...... 54
Table 5.3 De ini ions o weal h a iables ............................................................................. 55
Table 5.4 Summa y o main weal h a iables ...................................................................... 55
Table 5.5 Summa y o sa ings (€) ........................................................................................ 56
Table 5.6 Summa y o indeb edness .................................................................................... 56
Table 5.7 P opo ion o landlo ds unable o inance he in es men ac i i y by inancial
measu e and in es men size ............................................................................... 58
Table 5.8 Median in es men gap o landlo ds unable o inance he in es men ac i i y by
inancial measu e and in es men size ................................................................ 59
Table 5.9 Median mon hly epaymen amoun o landlo ds who equi e a loan o co e he
in es men gap ..................................................................................................... 60
Table 5.10 DSR o landlo ds wi h exis ing deb and epaymen s as pe in es men gaps ... 62
Table 5.11 Loan- o- alue a io (po olio le el) o landlo ds wi h exis ing deb and bo owing
abo e loan amoun s ............................................................................................. 62
Table A.1 Conco dance o dwelling ypes ............................................................................ 75
Table A.2 Mean es ima es o he en al housing s ock size ................................................. 75
Table A.3 Numbe o obse a ions in upg ade cos da a, p e- and pos -upg ade ............... 76
Table A.4 Cos o upg ade es ima ion esul s ...................................................................... 77
Table A.5 Es ima es o agg ega e cos s o upg ade o b* o all dwellings wi h be g o c1,
wi hou accoun ing o he i age buildings ........................................................... 77
Table B.1 Es ima ed pe cen age o he i age buildings by coun y and dwelling ype .......... 79
Table C.1 Cha ac e is ics o p ope ies wi h BER compa ed o wi hou BRT ....................... 80
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |iii
LIST OF FIGURES
Figu e 2.1 Tenu e s uc u e o I ish households om Census da a ........................................ 6
Figu e 2.2 BER s uc u es o di e en g oups o en al sec o (% o o al) ........................... 8
Figu e 2.3 P ope y age by landlo d ype (% o o al) ............................................................. 8
Figu e 3.1 RTB- egis e ed p ope ies ac oss dwelling ype................................................... 16
Figu e 3.2 RTB- egis e ed p ope ies by coun y ................................................................... 16
Figu e 3. 3 RTB- egis e ed p ope ies by numbe o bed ooms ............................................ 17
Figu e 3. 4 RTB- egis e ed p ope ies wi h sel - epo ed BER ............................................... 17
Figu e 3. 5 Dis ibu ion o sel - epo ed BER o RTB- egis e ed p ope ies .......................... 18
Figu e 3.6 BER dis ibu ion by coun y and dwelling ype ...................................................... 19
Figu e 3. 7 Numbe o assessmen s by assessmen pu pose ................................................. 20
Figu e 3.8 Numbe o assessmen s by yea o assessmen ................................................... 21
Figu e 3. 9 Numbe o assessmen s by dwelling ype ............................................................ 22
Figu e 3.10 Numbe o assessmen s by yea o cons uc ion ................................................. 22
Figu e 3.11 Numbe o assessmen s by loo a ea .................................................................. 23
Figu e 3.12 BER a ings dis ibu ion o assessmen s ............................................................... 23
Figu e 3.13 BER assessmen s dis ibu ion by yea o assessmen .......................................... 24
Figu e 3.14 Dis ibu ion o sample weigh s, WT ...................................................................... 28
Figu e 3.15 Dis ibu ion o BER a ings in RTB da a be o e and a e adjus men s ................ 29
Figu e 3.16 Dis ibu ion o BER a ings in SEAI da a be o e and a e adjus men s ............... 30
Figu e 3.17 Es ima ed en al housing s ock by BER ................................................................ 31
Figu e 4.1 Key cha ac e is ics by upg ade expendi u es da ase .......................................... 35
Figu e 4.2 Summa y o BER dis ibu ions in expendi u e da ase s ....................................... 37
Figu e 4.3 Es ima ed a e age cos s o upg ade by p e-upg ade be in €1k in 2023 p ices .. 41
Figu e 4.4 Cumula i e cos s and numbe o dwellings o upg ade es ima es ...................... 45
Figu e 5.1 P opo ion o households ha own o he p ope ies apa om main esidence
.............................................................................................................................. 49
Figu e 5.2 Pe cen o households owning o he p ope y by yea ...................................... 49
Figu e 5.3 Compa ing landlo ds and non-landlo ds: Age ...................................................... 50
2|In es men equi emen s o ene gy e iciency upg ades in he en al sec o
he in es men cos would be o upg ade he s ock o a highe ene gy e iciency
le el. Finally, we explo e whe he household landlo ds,
1
who make up a la ge
p opo ion o he owne s in he sec o , ha e he inancial capaci y o make hese
in es men s in ene gy e iciency. Mo e speci ically, we a emp o answe he
ollowing ques ions:
• Wha is he cu en ene gy-e iciency p o ile o he en al sec o in e ms o
he dwelling ypes, loca ions and p ope ies?
• Wha le el(s) o in es men would be equi ed o inc ease he abo e housing
s ock o mo e ene gy e icien le els?
• Do household landlo ds ha e he inancial capaci y o make in es men s in
ene gy e iciency?
Fo he inal ques ion, ou ocus is on household landlo ds o wo easons: i s ,
hey make up a la ge p opo ion o he ma ke ; and second, hey a e mo e likely
o ace ba ie s o in es men ela i e o la ge ins i u ional landlo ds. Fo example,
la ge , comme cial landlo ds ace di e en inancing condi ions and inancial
capaci y han smalle household landlo ds. They also ace di e ing incen i es
a ound he payback pe iod and a e o e u n on any esiden ial in es men
ac i i y. Howe e , he en al sec o has many household landlo ds who ha e ew
p ope ies and may no ha e su icien weal h o inance he upg ade plans.
Unde s anding he p o ile and s uc u e o hese landlo ds is c i ically impo an in
e ms o unde s anding he sec o ’s in es men ou look and capaci y. Ou
assessmen o he in es men capaci y o hese landlo ds is done om a inancial
pe spec i e and does no ake in o conside a ion he a ailabili y o exis ing policy
suppo s o which hey can a ail.
Add essing hese ques ions is hinde ed by se e al no able da a gaps. Fi s , he e is
no na ional da abase o all en al p ope ies ha would include e i ied BER
ce i ica es and would p o ide de ailed in o ma ion on he a ious p ope y
cha ac e is ics needed o explo e any po en ial upg ades. Second, in o ma ion is
equi ed on he ypical cos s o ene gy e iciency upg ades and he co esponding
change in he BER.
To b idge hese da a gaps, we d aw om a numbe o di e en da ase s bo h as
co e analy ical ools and also as seconda y obus ness checks. To p o ile he ene gy
e iciency o he sec o , we i s ly use da a om he annual and new enancies
egis a ions om he Residen ial Tenancies Boa d (RTB) o he 12-mon h pe iod
o Ap il 2022 o Ap il 2023. In o ma ion on new enancies has been collec ed since
2007. Howe e , annual egis a ion o all enancies in I eland has been a legal
equi emen since Ap il 2022. Mo e de ails on his da ase can be ound in
1
A household landlo d is de ined as a household ha owns a esiden ial p ope y o he han hei main household
esidence and en s ou o leases ha p ope y. No e his does no include ins i u ional landlo ds.

In oduc ion|3
Slaymake and Shiel (2023). In his da ase , we ob ain sel - epo ed BER ce i ica e
alues, which a e a ailable o app oxima ely hal o he p ope ies. The o e all
da ase size is app oxima ely 190,000 obse a ions.
2
The da ase also con ains
selec ed in o ma ion on he dwellings such as hei ype (semi-de ached, de ached,
e ace, apa men ) as well as loo a ea and loca ion.
As hese da a only con ain a sel - epo ed BER, we equi e a obus ness check o
c oss-examine he sel - epo ed in o ma ion. Fo hese pu poses, we also d aw on
he Sus ainable Ene gy Au ho i y o I eland (SEAI) p ope y-le el BER Resea ch Tool
da abase, which con ains a la ge ange o in o ma ion on he ene gy e iciency o
BER-assessed p ope ies. F om hese da a, a subse can be ex ac ed o he
p ope ies ha ha e had a BER assessmen o he pu poses o en ing he
p ope y on he p i a e ma ke . Fo bo h he RTB and SEAI da ase s, we mo e om
he sample o a popula ion es ima e by aking a se o weigh s by coun y and
p ope y ype om he I ish Census o Popula ion 2022. Addi ionally, we es ima e
he numbe o en al p ope ies in p o ec ed he i age buildings. These buildings
a e exemp om BER a ings and a e excluded om he cos es ima es.
To unde s and he cos o upg ading indi idual p ope ies in I eland, we combine
wo da ase s. Fi s , a da abase om he Depa men o Housing, Local Go e nmen
and He i age con ains in o ma ion on he in es men expendi u es on upg ading
p ope ies owned by he local au ho i ies as pa o hei social housing s ock. This
Local Au ho i ies Social Housing Upg ade (LASHU) da ase con ains de ailed
in o ma ion on he p ope y, including housing ype, in o ma ion on he upg ade
and, c i ically, he BER a ings be o e and a e he upg ade. Ou da ase con ains
390 upg ades ac oss 16 local au ho i ies o he yea 2022. Second, we use a
da ase , p o ided by he SEAI, ha includes a sample o p ope ies ha ha e
ecei ed g an s o he e iciency upg ades om he SEAI’s One S op Shop (OSS)
se ices. This da ase includes 1,068 upg ades by bo h p i a ely-owned p ope ies
and p ope ies o he app o ed housing bodies (AHBs).
F om he combined LASHU and OSS da a, we es ima e he a e age cos o
e iciency upg ades pe dwelling wi h mul iple eg ession models. We use he pos -
upg ade B a ings as he upg ade scena io. The upg ade cos s a e hen modelled
as a unc ion o p e-upg ade BER, loca ion, dwelling ype and dwelling size. The
agg ega e cos es ima es a e ob ained by combining pe -dwelling cos s es ima es
wi h he popula ion es ima es o he en al housings s ock. Finally, ou assessmen
o he inancial capaci y o household landlo ds is unde aken using he Cen al
S a is ic O ices (CSO) Household Finance and Consump ion Su ey (HFCS).
2
The e a e a numbe o easons why landlo ds may no epo hei BER, such as lack o awa eness o he in o ma ion,
acciden al missing da a o possible o he s a egic easons. We do no ha e any da a ha p o ides insigh in o hese
ac o s a p esen . These da a a e desc ibed in mo e de ail in Sec ion 3.
4|In es men equi emen s o ene gy e iciency upg ades in he en al sec o
The pape is s uc u ed as ollows. Chap e 2 p esen s he in e na ional li e a u e
and I ish con ex . Chap e 3 p o iles he ene gy e iciency o he sec o . Chap e 4
conside s in es men equi emen s o upg ades. Chap e 5 explo es he inancial
capaci y o household landlo ds. Chap e 6 concludes.
Backg ound and con ex |5
CHAPTER 2
Backg ound and con ex
2.1 THE IRISH HOUSING MARKET
In line wi h he challenges in he b oade housing ma ke in I eland, he p i a e
en al sec o (PRS) has been p o iding a g owing sha e o housing in he pas
numbe o yea s. Figu e 2.1 p esen s he enu e s uc u e o I ish households om
he Census om 1991 onwa ds. The igu e p esen s h ee g oups o households:
hose in local au ho i y housing; p i a e en ing households, which include en als
om app o ed housing bodies (AHBs); and owne -occupied housing. The sha e o
owne -occupied housing has declined om a high o 82 pe cen in 1991 o 70 pe
cen in 2022. This decline has seen a co esponding inc ease in he sha e o p i a e
en al p ope ies. Two u he insigh s om he Census da a a e impo an in e ms
o con ex ualising he changing ole o he PRS: Figu e 2.1 also shows he
p opo ion o households wi h child en, o e all and o households li ing in he
PRS, as well as he p opo ion o households in he PRS ac oss he age dis ibu ion
o he household. The la ges inc eases in he p opo ion o en e s ac oss he age
dis ibu ion a e occu ing among hose in he ‘ amily o ma ion’ age g oup (30–44
yea s). The a e is also inc easing among olde en e s (65+ yea s). Fu he mo e,
he p opo ion o households wi h child en in he en al sec o inc eased be ween
2011 and 2022. This changing demog aphic s uc u e may p esen g ea e
challenges in e ms o he ene gy e iciency equi emen s o he dwellings o e
ime.
6|In es men equi emen s o ene gy e iciency upg ades in he en al sec o
FIGURE 2.1 TENURE STRUCTURE OF IRISH HOUSEHOLDS FROM CENSUS DATA
A: O e all enu e s uc u e
B: P opo ion o households wi h child en
C: Age dis ibu ion o en e s
Sou ce: CSO Census da a.
No es: Owne -occupied housing includes hose owned ei he by mo gage o ou igh . Dwellings occupied ee o en and
hose o whom ‘no s a ed’ is eco ded ega ding he na u e o he occupancy a e excluded.
82% 81% 78% 72% 71% 70%
8% 12% 14% 20% 20% 21%
10% 7% 8% 8% 9% 9%
0%
20%
40%
60%
80%
100%
1991 2002 2006 2011 2016 2022
Local auho i ies en als P i a e and AHB en als Owne -occupied
47% 47% 45%
35%
41% 37%
0%
20%
40%
60%
2011 2016 2022
All ypes o occupancy P i a e en al sec o
0%
20%
40%
60%
80%
Unde 25
yea s
25 - 29
yea s
30 - 34
yea s
35 - 39
yea s
40 - 44
yea s
45 - 49
yea s
50 - 54
yea s
55 - 59
yea s
60 - 64
yea s
65 yea s
and o e
2011 Census 2016 Census 2022 Census
Backg ound and con ex |7
A numbe o esea ch pape s ha e s udied hese dynamics. McQuinn e al. (2021)
and Slaymake e al. (2022) ha e bo h no ed ha he challenges in e ms o
homeowne ship ha e been due o house p ices ou s ipping income g ow h o
young households and housing supply emaining well below he le el needed o
s ands ill household o ma ion. Indeed, Slaymake e al. (2022) indica e ha a
s uc u ally lowe a e o homeowne ship is likely o con inue, wi h mo e
households emaining in en ed accommoda ion h oughou hei li ecycle.
F om he pe spec i e o his esea ch, he c i ical poin is ha a g ea e sha e o
he housing s ock is now o en al accommoda ion. Fo his eason, he challenge
o managing he spli incen i e in an ene gy e iciency con ex is e en g ea e han
i would ha e been his o ically.
A u he complica ion a ises due o he quali y o he housing s ock in he en al
sec o in I eland. The p opo ion o p ope ies wi h low ene gy e iciency and a
e y high e o i equi emen is highe han ha o he owne -occupied sec o
(Pe o and Ryan, 2021). This is due o he na u e o he housing s ock in he PRS,
which ends o be olde and o ha e a lowe building ene gy a ing (BER). Figu e
2.2 p esen s he s uc u e o he BER a ings o p ope ies in di e en g oups o
he en al sec o om he Cen al S a is ics O ice (CSO) epo , Ren al ma ke in
I eland 2021. These da a spli he sec o in o di e en g oups o he ma ke based
on BER s a us and he ype o landlo d. I mus be no ed ha hese da a a e aken
om he Residen ial Tenancies Boa d (RTB) esiden ial enancies egis a ion
da ase , which a ha ime co e ed only new egis a ions and Pa 4 enewals. I
does no he e o e p o ide an assessmen o he en i e y o he en al housing
s ock; a he , i o e samples p ope ies new o he ma ke (including new
cons uc ion) and hose ha u n o e on a egula basis.
The g oupings p esen ed a e: app o ed housing bodies (AHBs), Housing Assis ance
Paymen (HAP) ecipien s, Ren Supplemen ecipien s,
3
local au ho i y p ope ies,
PRS housing wi h p i a e indi idual household landlo ds and PRS housing wi h non-
household owne s (such as in es men unds and o he ins i u ional landlo ds). I
is e y clea ha he indi idual landlo d owned p ope ies, as well as hose
inhabi ed by HAP and Ren Supplemen ecipien s, who also li e in PRS
accommoda ion, ha e he highes sha e o low BER p ope ies; a leas 50 pe cen
o he p ope ies in hese g oupings a e below a C a ing. Bo h AHB p ope ies and
hose in he non-household PRS sec o ha e a highe sha e o A–C BER- a ed
p ope ies, mainly due o he ac ha his housing s ock ends o be newe .
3
No e bo h HAP and Ren Supplemen ecipien s li e in PRS housing.

8|In es men equi emen s o ene gy e iciency upg ades in he en al sec o
FIGURE 2.2 BER STRUCTURES FOR DIFFERENT GROUPS OF RENTAL SECTOR (% OF TOTAL)
Sou ce: CSO da a o 2020.
No es: The RTB da a only ela e o new enancies and Pa 4 enewals, as he RTB did no collec annual egis a ions o
he pe iod in which he CSO unde ook his analysis. PRS=P i a e en al sec o . No e bo h HAP and Ren Supplemen
ecipien s li e in p i a e en al sec o housing. The ca ego y o ‘indi idual landlo d’ used he e e e s o hose
landlo ds who egis e ed wi h he RTB using a PPS numbe , while he non-household landlo ds used a company
egis a ion o ice numbe .
This can be seen mo e clea ly in Figu e 2.3, which p esen s he age o p ope ies
owned by he same g oupings o landlo ds. The majo i y o p ope ies owned by
he AHB sec o as well as he non-household PRS sec o we e buil pos 2000,
whe eas his sha e is much lowe o indi idual PRS p o ide s as well as he local
au ho i y housing s ock.
FIGURE 2.3 PROPERTY AGE BY LANDLORD TYPE (% OF TOTAL)
Sou ce: CSO da a o 2020.
No es: The RTB da a only ela e o new enancies and Pa 4 enewals, as he RTB did no collec annual egis a ions o
he pe iod in which he CSO unde ook his analysis. PRS=P i a e en al sec o . No e bo h HAP and Ren Supplemen
ecipien s li e in PRS housing.
13 11110 2
28
812 9
22
10
34
40 46 40
34
36
19
40
34 40
27
39
612 710 713
0
20
40
60
80
100
AHB HAP Local au ho i ies PRS indi idual
landlo ds
PRS non-
household
landlo ds
Ren Supplemen
housing
A B C D-E F-G
171757
3
14 13 12 613
6
15 38 15
5
15
24
21
16
23
13
22
54
43 31 42
60
41
12 11111 2
0
20
40
60
80
100
AHB HAP Local au ho i ies PRS indi idual
landlo ds
PRS non-
household
landlo ds
Ren Supplemen
housing
Be o e 1919 1919 o 1970 1971 o 1990 1991 o 2000 2001 o 2010 2011 o la e
Backg ound and con ex |9
These da a indica e a conside able in es men challenge o he sec o , and in
pa icula o indi idual landlo ds, i ene gy e iciency commi men s a e going o
be me by he sec o .
2.2 INTERNATIONAL LITERATURE
The issue o in es men in ene gy e iciency echnologies has come o he o e in
ecen yea s in line wi h aims o ansi ion o low ca bon economies
in e na ionally. Exis ing esea ch indica es a gene al ‘ene gy-e iciency gap’,
whe eby he economic le el o in es men sugges ed by cos minimising (o ene gy
sa ing) le els is well below ha which is ac ually unde aken by households and
i ms. Allco and G eens one (2012) p o ide a de ailed discussion o his issue and
no e ha he ‘win–win’ a gumen o in es men in ene gy-sa ing echnology is
ha i can sa e ossil uels ( hus educing all he ha m ul ex e nali ies ha come
om hei usage) as well as help b idge an ine icien le el o ma ke in es men
by pa icipan s.
These wo concep s in e wine wo se s o ma ke ailu e, which a e impo an o
sepa a e ou when ying o unde s and he unde in es men in ene gy e iciency.
The i s is he issue o nega i e ex e nali ies o he p oduc ion o ossil uels; he
second conce ns ou unde s anding o he ba ie s o in es men and he ex en
o which in o ma ion asymme ies o o he ic ions such as c edi ma ke
impe ec ions a e d i ing in es men choices. Allco and G eens one (2012) no e
he policy esponse a ies depending on he wo ma ke ailu es; Pigou ian axes
o cap and ade p og ammes can be used o ex e nali ies, whe eas o he
ins umen s o subsidise o manda e ene gy e iciency can be used o add ess he
unde in es men .
In he con ex o his pa icula esea ch, ou ocus is on he ma ke ailu es ela ing
o unde in es men in he esiden ial eal es a e ma ke . The ypes o ma ke
ailu es ha can occu in his case a e no ed by Allco and G eens one (2012):
in o ma ion impe ec ions (a lack o in o ma ion on wha he op imal le el o
in es men is o an indi idual household); ina en ion (missing key elemen s o
he choice decision du ing he pu chase decision); c edi ma ke access; and mo al
haza d. Gi en hese ac o s, Allco and G eens one (2012) no e ha he e is likely
a e y di e en ia ed he e ogenei y in in es men ine iciencies ac oss he
popula ion, hus policy a ge ing is equi ed o deal wi h he di e en ia ed
challenges. Howe e , challenges ha e been ound in designing and implemen ing
hese policies in e na ionally, such as a emp ing o use a ge ed ins umen s ha
do no ha e he desi ed impac ( o example, Mu phy e al., (2012) documen hese
issues o he Ne he lands by no ing ha he policies do no ake in o conside a ion
he complexi y in ol ed wi h ega d o exis ing dwellings).
These in es men ine iciencies a e all he mo e acu e in he en al side o he
housing sec o due o he ‘spli incen i e’ p oblem. This issue ela es o he
10|In es men equi emen s o ene gy e iciency upg ades in he en al sec o
si ua ion whe eby landlo ds a e he in es o s, bu he enan s a e he ones who
eap he ewa d h ough lowe ene gy bills o o he ene gy-sa ing bene i s. This
spli incen i e makes all he abo e ma ke ailu es mo e acu e and challenging o
o e come. Cas ellazzi e al. (2017) no e ou speci ic ypes o spli incen i es:
• e iciency- ela ed spli incen i es ( he enan pays he elec ici y bills bu
canno choose he echnology o imp o e he e iciency);
• usage- ela ed spli incen i es (when occupan s a e no esponsible o paying
hei u ili y bills and he e o e ha e li le o no in e es o conse e ene gy);
• mul i- enan , mul i-owne spli incen i es ( his occu s whe e consensus is
equi ed o ene gy e iciency upg ades amongs a he e ogeneous g oup o
enan s/owne s); and
• empo al spli incen i es (whe e he ene gy e iciency in es men will no pay
o be o e he p ope y ge s ans e ed ac oss owne ship).
Some esea ch has ound ha ene gy pe o mance ce i ica es can mi iga e some
o hese issues. Dwellings wi h highe le els o ene gy e iciency ha e a highe sales
alue, as well as a highe en al alue (Fue s e al., 2020). Howe e , Co nago and
D essle (2020) documen ha landlo ds do no always disclose he ene gy
ce i ica es o enan s e en i he ce i ica e exis , and ha many p ospec i e
enan s do no p ope ly accoun o ene gy cos s when deciding on which p ope y
o en .
Ás ma sson e al. (2013) no e ha his misalignmen o in e es s is one o he
g ea es ba ie s hinde ing he in es men in sus ainabili y om an ene gy
e iciency pe spec i e in esiden ial buildings in Eu ope. A oluminous li e a u e
explo es his issue in e na ionally. A ecen sys ema ic e iew o he li e a u e by
Lang e al. (2021) no es he poo e ene gy e iciency o en ed homes o owne -
occupied p ope ies in many coun ies ac oss Eu ope, No h Ame ica and
Aus alasia. They no e ha small-scale landlo ds a e he key decision make s and
e y li le is known abou hei decisions. Looking ac oss 16 pape s, hey ind ha
47 ac o s ha e been no ed as de e mining hei beha iou , including inancial
ac o s, alues, belie s, p ope y-ma ke ac o s and o he aspec s o hei
ela ionships wi h enan s.
Nie e al. (2020) explo e he adop ion o ene gy-sa ing measu es be ween
homeowne s and en e s in a su ey o 1,248 households ac oss h ee coun ies
(Ge many, Ne he lands and Belgium). They ind clea e idence o spli incen i e
p oblems in ela ion o bo h ene gy e icien echnology adop ion and ene gy-
sa ing beha iou s. They ind ha homeowne s a e 16 pe cen mo e likely han
en e s o adop hese echnologies, hough wi h a lowe di e ence ega ding
beha iou al measu es.
Backg ound and con ex |11
Some o he easons o non-in es men in ene gy e iciency by landlo ds a e no ed
in a pape on he UK ma ke by Hope and Boo h (2014). They s udy he easons o
landlo ds choosing no o in es in ene gy e iciency echnologies, inding ha he
majo i y (67 pe cen ) indica e ‘high-up on cos s’; o he no able epo ed ac o s
include ‘ enan s a e happy wi h he ene gy e iciency’ and ‘no pe sonal bene i o
making imp o emen s’ (40 pe cen ). Access o inance o lack o in o ma ion we e
no no ed as ba ie s in hei esea ch. These indings a e also echoed in esea ch
by Amb ose (2015) who unde ook a esea ch in e iew wi h 30 landlo ds in
no he n England and iden i ied hese ele an issues: spli incen i es, ime cos s,
bu den and in o ma ion on he op ions.
Fu he esea ch by Miu and Hawkins (2020) su eys he e o i beha iou o
p i a e landlo ds in he UK and assesses hei engagemen ac oss 18 di e en
ene gy e iciency measu es. They g oup landlo ds in o se en beha iou al
ypologies o landlo d e o i e s, and sugges a segmen a ion o he landlo d
popula ion in o di e en a ge g oups o he e ogeneous policy in e en ions.
They no e ha ailo ing policy can be e deal wi h a numbe o issues including
policy suppo ake-up, inc easing he likelihood o e o i and accele a ing he
ene gy-e iciency ansi ion.
Fu he e idence is also a ailable o suppo policy ins umen combina ions o
deal wi h his issue. In esea ch on he Danish en al sec o , Ás ma sson e al.
(2013) ind ha hese p incipal agen p oblems can only be o e come wi h a
package solu ion ha includes legisla i e changes, inancial incen i es and be e
dissemina ion o in o ma ion.
In an a emp o p o ide a c oss-coun y solu ion o he in o ma ional asymme ies
componen o he ene gy e iciency gap in en al housing, a majo EU- unded
esea ch p ojec Ren Cal p oduced a ool ha can help b eak down in o ma ion
ba ie s (Zei le , 2018).
O he s udies look a di e en aspec s o he egula ions used o incen i ise
in es men s in ene gy e icien echnologies. Fo Ge many, Webe and Wol
(2018) ind ha landlo ds pass on in es men cos s o enan s as is allowable unde
en con ol legisla ion, and hese cos s a e highe han he ene gy e iciency
sa ings. This is an impo an inding in an I ish con ex as such an exemp ion is
allowable in Ren P essu e Zone a eas. Cha lie (2015), in a s udy on F ench da a,
shows enan s a e lowe income and unable o in es due o insu icien unds.
Ma uejols and Young (2011) use Canadian da a and ind ha enan s’ beha iou
depends on whe he hey ace he cos o ene gy usage amoun s.
18|P o iling he ene gy e iciency o he p i a e en al sec o
Figu e 3.5 p esen s he dis ibu ion o BER a ings ac oss hose p ope ies lis ed as
ha ing a sel - epo ed BER. I shows ha e y ew p ope ies in he I ish PRS ha e
an A a ing; jus o e 10 pe cen o he p ope ies a e lis ed as ha ing an A a ing,
wi h ewe han 1 pe cen ha ing an A1 a ing. In e ms o B- a ed PRS p ope ies,
8 pe cen ha e a B3 a ing, 4.6 pe cen ha e a B2 a ing and 2.9 pe cen ha e a
B1 a ing, o alling 15.5 pe cen o p ope ies wi h an o e all B a ing. As he
ene gy e iciency equi emen s a e likely o encou age dwellings o be a leas B
a ed, hese da a highligh he conside able challenge acing he sec o in e ms o
in es ing su icien ly o each his pa icula le el. Indeed, acco ding o hese da a,
jus unde h ee in e e y ou p ope ies in he en al sec o do no mee a B a ing.
A majo i y o p ope ies ha e ei he a C o D a ing; 13 pe cen ha e a C1 a ing,
12 pe cen ha e a C2 a ing, and 14 pe cen ha e a C3 a ing, o alling 38 pe cen .
Rega ding D- a ed p ope ies, 12.6 pe cen ha e a D1 a ing, while 9.6 pe cen
ha e a D2 a ing. Focusing in on he lowes a ed p ope ies, which a e likely o
ha e he g ea es challenge in e ms o he ene gy e iciency in es men
equi emen s, 8.2 pe cen ha e an E a ing, 2.9 pe cen ha e an F a ing and 3 pe
cen ha e a G a ing – he lowes possible BER.
6
FIGURE 3. 5 DISTRIBUTION OF SELF-REPORTED BER OF RTB-REGISTERED PROPERTIES
Sou ce: RTB Regis a ions da a.
No e: Excluding p ope ies wi hou sel - epo ed BER. Da a wi hou adjus men s.
In o de o p o ide mo e g anula de ail on which p ope ies ha e di e en BER
a ings and whe e hose p ope ies a e loca ed, Figu e 3.6 p esen s high-le el BER
dis ibu ions ac oss p ope y ypes and ac oss coun ies. Focusing on he
geog aphic spli , da a a e p esen ed o Dublin, Co k, Galway, Lime ick, Wa e o d
and ‘O he coun ies combined’. I is clea ha mo e o he A- a ed p ope ies a e
loca ed in Dublin; his likely e lec s he ac ha in ecen yea s Dublin has
accoun ed o a g ea e p opo ion o new housing supply in he en al sec o ,
6
Fo any u he in o ma ion on he BER scale e c, please see: h ps://www.seai.ie/publica ions/You -Guide- o-
Building-Ene gy-Ra ing.pd .

In es men equi emen s o ene gy e iciency upg ades in he en al sec o |19
many o which a e new, build- o- en p ope ies (Daly, 2023). These newly
cons uc ed p ope ies will ha e been buil unde he cu en highe ene gy a ing
s anda ds. Co k has he second highes sha e o B- o highe a ed p ope ies a e
Dublin. Galway and Lime ick a e he a eas wi h he g ea es p opo ion o D o
lowe a ings in he da a. Figu e 3.6 also p esen s he high le el a ings by p ope y
ype: apa men , de ached, semi-de ached and e aced. Apa men s ep esen
he mos ene gy e icien g oup, wi h he highes sha e o A- o B- a ed p ope ies.
Houses, o any ype, had ewe han 20 pe cen o he s ock a B o highe a ing
bu app oxima ely 60 pe cen ac oss hese g oups had a C o highe a ing.
FIGURE 3.6 BER DISTRIBUTION BY COUNTY AND DWELLING TYPE
Coun y
Dwelling ype
Sou ce: RTB Regis a ions da a.
3.3 SEAI BER DATABASE
The RTB da ase desc ibed abo e is he la ges sample a ailable on he en al
sec o a he mic o le el, wi h ene gy e iciency indica o s. Howe e , due o he
sel - epo ed na u e o he in o ma ion and he la ge p opo ion o non- epo ed
a ings, he e may be some biases in he in o ma ion, whe eby landlo ds may
mis epo he ue a ing o whe e he da a may be missing sys ema ically.
To a emp o p o ide a obus ness check agains his occu ence, we d aw on a
second da a sou ce: he SEAI BER Resea ch Tool mic o da abase, which is made
a ailable by he SEAI o esea ch pu poses. While hese da a do no con ain a
speci ic indica o o whe he a p ope y is cu en ly being en ed, o p o ide a
s ock o en al ma ke p ope ies, hey do ha e some use ul in o ma ion ha we
can d aw on. These da a allow us o iden i y hose p ope ies o which he pu pose
o he BER ce i ica e applica ion was o ‘p i a e le ing’. We assume hese
p ope ies a e ac i e and in he en al sec o . I also pools all da a ac oss he yea s
o he BER (2009–2023 in ou sample).
20|P o iling he ene gy e iciency o he p i a e en al sec o
FIGURE 3. 7 NUMBER OF ASSESSMENTS BY ASSESSMENT PURPOSE
Sou ce: SEAI BER Resea ch da a.
The da abase p o ided by SEAI con ains ex ensi e in o ma ion ha was cap u ed
as pa o he BER p ocess. The esea ch ool p o ides da a on he BER scheme o
app oxima ely 1.1mn obse a ions. I includes all in o ma ion collec ed as pa o
he BER p ocess: ene gy pe o mance o he dwelling;
7
hea ing; en ila ion;
ligh ing; and p ope y cha ac e is ics, e c. The da a a e anonymised; o example,
he me e poin e e ence numbe (MPRN), name(s) and add ess ha e all been
emo ed om each en y. C i ically o he pu poses o ou esea ch, a numbe o
ele an ields ( he BER no wi hs anding) a e included. These include: yea o
cons uc ion, ype o p ope y, pu pose o BER ce i ica e (sale, en e c.), and yea
o applica ion.
As no ed abo e, using hese da a, we can iden i y a subse o 82,299 obse a ions,
om he o e all da abase, ha ela e o en al p ope ies only. These a e he
p ope ies whose decla ed pu pose was ha he BER was ob ained o p i a e
le ing (i.e. he BER was applied o because he p ope y was o become pa o
he PRS). The easons p ope y owne s ga e o seeking a BER ce i ica e a e
p esen ed in Figu e 3.7. I is clea he as majo i y o he BER a ings we e
ob ained o o he pu poses ( o example sale, g an suppo , owne occupa ion,
e c). A numbe o poin s a e wo h no ing. P ope ies ha a e cu en ly in he
en al sec o could ha e ob ained a BER ce i ica e h ough a sale p ocess, o om
a g an applica ion e c. Only using he g oup o p ope ies ha sough a BER a ing
o he speci ic pu pose o en ing he p ope y in ou sample means we exclude
7
The BER ce i ica e p o ides a measu ed scale o A–G, which gi es an ene gy pe o mance sco e ha is compa able
ac oss p ope ies (wi h A being he highes ene gy e iciency). Each p ope y is p o ided a sco e o ene gy use pe
uni loo a ea pe yea (kWh/m2/y ). Fo an example, please see: h ps://www.seai.ie/home-ene gy/building-
ene gy- a ing-be /unde s and-a-be - a ing/Sample-BER-Ce .pd .
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |21
hese g oups. Howe e , his was una oidable, as we a e unable o iden i y en al
sec o p ope ies om wi hin he o he ca ego ies.
The e is a second impo an conside a ion. P ope ies could ha e been in he
owne -occupied ma ke and hen ans e ed o he en al sec o o om he
en al sec o o owne occupa ion. We he e o e canno de e mine whe he o no
hese 82,299 p ope ies a e s ill in he en al sec o a p esen . I mus also be
no ed ha a single p ope y could ha e mul iple BER assessmen s, in which case i
would he e o e appea mul iple imes in he da a. Despi e hese limi a ions, we
use hese da a as a obus ness check on he RTB da a, which do no su e om
hese en y and exi challenges.
FIGURE 3.8 NUMBER OF ASSESSMENTS BY YEAR OF ASSESSMENT
Sou ce: SEAI BER Resea ch da a.
The yea o comple ion o he BER ce i ica es a e p esen ed in Figu e 3.8. I shows
ha he p i a ely le p ope ies in he sample had hei BER assessmen comple ed
a di e en poin s in ime. The igu e co e s p i a ely le p ope ies and all o he
pu poses combined. While o e all a g ea e p opo ion o BER ce i ica es ha e
been ob ained in mo e ecen yea s, in he p i a ely-le sample, mo e han one-
hi d o assessmen s a e om 2014 o ea lie .
Figu e 3.9 p esen s p ope ies o p i a e en al and o he pu poses ac oss a
numbe o housing s ock ca ego ies: apa men s, de ached houses, semi-de ached
houses, e aced houses and o he . The p i a e le ings da a a e much mo e
skewed owa ds apa men s, wi h jus o e 50 pe cen o he obse a ions coming
om his housing ype. The e a e ewe de ached and semi-de ached houses in he
en al sample compa ed o he ‘o he pu poses’ sample.
22|P o iling he ene gy e iciency o he p i a e en al sec o
FIGURE 3. 9 NUMBER OF ASSESSMENTS BY DWELLING TYPE
Sou ce: SEAI BER Resea ch da a.
FIGURE 3.10 NUMBER OF ASSESSMENTS BY YEAR OF CONSTRUCTION
Sou ce: SEAI BER Resea ch da a.
An in e es ing ac o a ailable om he SEAI da a ha is no a ailable in he RTB
da a is p ope y age. Olde p ope ies a e likely o be o poo quali y ega ding
ene gy e iciency, i hey ha e no been upg aded. The e o e, his is an impo an
a iable in e ms o p o iding insigh in o ou unde s anding o he in es men
equi emen s o he sec o . Figu e 3.10 shows he age dis ibu ion o p i a ely
en ed and o he p ope ies by BER s a us. Two in e es ing ends eme ge: he e
a e mo e e y old p ope ies (p e-1900) in he en al sec o ; and ewe p i a ely-
le p ope ies we e buil du ing he 1960s, 1970s and 1980s. Tha pe iod (1960s o
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |23
1980s) saw a majo expansion in homeowne ship in I eland; many o he new
builds om ha e a a e likely o ha e emained in ha enu e ca ego y. By
con as , he 2000s saw a g ea e p opo ion o p i a ely-le p ope ies being buil ,
as i was du ing ha decade ha buy- o-le s became a majo pa o he I ish
housing ma ke .
FIGURE 3.11 NUMBER OF ASSESSMENTS BY FLOOR AREA
Sou ce: SEAI BER Resea ch da a.
Figu e 3.11 shows he size dis ibu ion o p i a e en al BER a ings and he es
o he da ase . The me ic p esen ed is he loo a ea in me es squa ed. Two
o e laid his og ams a e p esen ed, wi h he blue da a ep esen ing he en al
sec o . These da a indica e ha he p ope ies in he en al sec o a e ypically
smalle han hei equi alen s in he o he ca ego ies.
FIGURE 3.12 BER RATINGS DISTRIBUTION OF ASSESSMENTS
Sou ce: SEAI BER esea ch da a.

24|P o iling he ene gy e iciency o he p i a e en al sec o
Finally, and o c i ical impo ance, is he BER dis ibu ion associa ed wi h hese
da a. This is p esen ed in Figu e 3.12. I shows he e a e disp opo iona ely mo e
C-, D- and E- a ed p ope ies in he en al sec o da a, wi h no ably ewe A a ed
p ope ies.
One possible eason o he o e all lowe ene gy e iciency in he SEAI da a,
compa ed o he RTB da a, is ha he SEAI sample includes his o ic da a. Figu e
3.13 shows he change in he dis ibu ion o BER a ings o e he yea s o
assessmen . The e is a no able inc ease in ene gy e iciency in bo h he en al
sec o and in he ‘o he pu poses’ g oups. The igu e also shows ha he en al
sec o ’s ene gy e iciency is lagging behind ha o buildings assessed o o he
pu poses.
FIGURE 3.13 BER ASSESSMENTS DISTRIBUTION BY YEAR OF ASSESSMENT
P i a e le ings
O he pu poses
Sou ce: SEAI BER Resea ch da a.
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |25
3.4 CENSUS RECONCILIATION AND COMPARATIVE ANALYSIS
Ha ing e iewed bo h he RTB and SEAI da ase s, ou nex goal is o es ima e he
o al numbe o dwellings in he PRS a each BER le el. In doing so, we also make
adjus men s o he da a o accoun o p o ec ed he i age buildings. These
buildings a e BER exemp and es ic ions apply ega ding he ypes o po en ial
ene gy e iciency upg ades ha could be ca ied ou on hem, which would likely
impac he upg ade cos s. These p o ec ed buildings a e he e o e ou side o he
scope o his epo , which uses he cu en Na ional Re o i Plan, published as
pa o Clima e Ac ion Plan 2021, as i s baseline con ex . The de ails o his analysis
a e p esen ed in Appendix B.
3.4.1 Residen ial Tenancies Boa d da a
As shown in Figu e 3.4, 52 pe cen o obse a ions in ou RTB da ase do no
include a BER a ing. One o he challenges he e is he po en ial o bias in he sel -
epo ed BER dis ibu ion. Fo example, i is possible ha some landlo ds wi h less
ene gy-e icien p ope ies may no epo hei BER a ing, which would bias ou
sample dis ibu ion owa ds ha ing a highe a ing han is he case o he ac ual
popula ion o p ope ies in he sec o . Fu he mo e, he e could be impac s o bias
whe eby hose p ope ies complying wi h RTB egis a ion in he i s place may be
mo e likely o ha e a high BER and o epo i . The e a e also likely o be o he
con ounding e ec s ha can impac he dis ibu ion o sel - epo ed BER a ings,
which a e no lis ed he e (such as economic o legal a iables ha impac he
p e e ence o he landlo d o compliance wi h he egis a ion p ocess).
Two biases a e he e o e wo h conside ing. The i s is whe he he da a ha
includes sel - epo ed BER a ings, in he RTB da a, a e sys ema ically di e en
om hose which do no . The second is whe he he RTB sample is ep esen a i e
o he o e all popula ion o en al p ope ies. To explo e he i s issue, we p esen
a numbe o ables ha compa e p ope ies wi h a sel - epo ed BER a ing agains
hose ha do no ha e a sel - epo ed BER. I any majo sys ema ic di e ences a e
ound o exis be ween he wo g oups, his would suppo he possibili y o bias
in he BER epo ing.
The i s se o cha ac e is ics conside ed in ou assessmen a e as ollows: loo
a ea; mon hly en ; numbe o enan s; numbe o bed ooms; and p ope y ype.
The da a a e p esen ed in Table 3.2. The obse a ions o ‘no BER’ ha e lowe en
and a e also smalle in e ms o loo a ea, numbe o enan s and numbe o
bed ooms. The e a e p opo ionally mo e de ached houses wi hou a epo ed BER
han wi h one (10.3 pe cen o 9 pe cen ). The e is a highe sha e o apa men s
wi h a epo ed BER, wi h apa men s making up 51.6 pe cen o he sample wi h
a BER a ing, compa ed o 50.5 pe cen o he wi hou one. The p opo ions o
semi-de ached and e ace houses a e simila in bo h samples, wi h no s a is ically
signi ican di e ence.
26|P o iling he ene gy e iciency o he p i a e en al sec o
TABLE 3.2 CHARACTERISTICS OF PROPERTIES WITH BER COMPARED TO WITHOUT BER
Va iable
No BER
Wi h BER
Di e ence
Floo a ea
88.92
89.6
-0.68***
Mon hly en
1302.2
1489.8
-187.7***
Numbe o enan s
1.828
1.886
-0.058***
Numbe o bed ooms
2.416
2.475
-0.059***
Apa men s
0.505
0.516
-0.011***
De ached
0.103
0.090
0.014***
Semi-de ached
0.235
0.238
-0.003
Te aced
0.157
0.157
0.000
Sou ce: RTB Regis a ions mic oda a.
No es: *** signi ican a 1 pe cen le el using - es . Floo a ea immed 5, 95 pe cen o ou lie s.
We now conside he di e ences be ween p ope ies wi h epo ed BER s a us
e sus hose wi hou his on a geog aphic basis. These a e p esen ed in Table 3.3.
In Dublin, he e is a no ably highe p opo ion o p ope ies wi h a epo ed BER
s a us (Dublin makes up 46 pe cen o he o al ‘wi h BER’ sample) han wi hou
(Dublin p ope ies comp ise 40 pe cen o he ‘wi hou BER’ sample) compa ed o
he b eakdown in o he a eas. In Co k, he e is a highe p opo ion o p ope ies
wi hou a epo ed BER s a us (Co k accoun s o 12.3 pe cen o he o al ‘wi hou
BER’ sample) han p ope ies wi h one (Co k makes up 9.9 pe cen o he o al
‘wi h BER’ sample). The e a e also di e ences in he o he a eas p esen ed, wi h
Galway making up a compa a i ely highe sha e o he ‘wi h BER’ sample and
Lime ick, Wa e o d and he es o he coun y accoun ing o a compa a i ely
highe sha e o he ‘wi hou BER’ sample.
TABLE 3.3 CHARACTERISTICS OF PROPERTIES WITH BER COMPARED TO WITHOUT BER
Coun y
Wi hou BER
Wi h BER
Di e ence
Co. Dublin
0.406
0.461
-0.055***
Co. Co k
0.123
0.099
0.024***
Co. Galway
0.054
0.061
-0.008***
Co. Lime ick
0.045
0.031
0.014***
Co. Wa e o d
0.026
0.020
0.006***
Res o he coun y
0.346
0.327
0.019***
Sou ce: RTB Regis a ions mic oda a.
No es: *** signi ican a 1 pe cen le el using - es / Floo a ea immed 5, 95 pe cen o ou lie s.
Tables 3.2 and 3.3 clea ly show ha di e ences exis be ween he p ope ies which
ha e and do no ha e a sel - epo ed BER a ing, based on obse able
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |27
cha ac e is ics. Fo his eason, we p opose he ollowing me hodology o deal
wi h his issue, based on de eloping a se o p obabili y weigh s. We i s de ine a
dummy a iable which akes he alue o 1 o hose p ope ies which ha e a sel -
epo ed BER, and 0 o he wise:
𝐻𝑎𝑠𝐵𝐸𝑅 = { 1 i BER epo ed
0 o he wise
We hen un a eg ession model o he p obabili y o no ha ing a BER a ing as a
unc ion o obse able cha ac e is ics. In ou lis o obse able cha ac e is ics, we
include he ollowing: he loo a ea and en amoun as le els and hei squa ed
e ms, p ope y ype dummies, and indica o a iables o he coun y:
P (HasBER=0)=𝑓( en , en 2, loo , loo 2 , dwelling ype , u ban , coun y)
This p obabili y is es ima ed as a logi model, wi h he esul s shown in he
appendix.
8
Following he es ima ion, we p edic o each p ope y he p obabili y
o ha ing a sel - epo ed BER 𝑝𝑖 based on he cha ac e is ics in he eg ession and
he es ima ed coe icien s. We hen use hese p edic ed p obabili ies o e-weigh
he sample.
The esul ing dis ibu ion is shown in he middle columns in Figu e 3.15. In he e-
weigh ed sample, he p opo ion o A* and B* a ings is lowe han be o e he
adjus men , while inc eases a e seen o C* and D* a ings. The p opo ion o
p ope ies a ed E and below emains simila .
A inal sample adjus men ha we make is o u he e-weigh he RTB sample by
coun y and dwelling ype, such ha he numbe o obse a ions in he RTB sample
co esponds o he Census 2022 da a by coun y and dwelling ype (o which he e
we e 330,632 dwellings in he PRS). We a emp o ma ch he da a as closely as
possible in e ms o dwelling ypes bu common g oupings a e equi ed. The
mapping ha co esponds he RTB da a and he Census da a is p esen ed in Table
A.1.
Fu he mo e, we make a numbe o adjus men s o accoun o he BER-exemp
s a us o he p o ec ed he i age buildings in he RTB da a. Fi s , we de elop a
p ocess ha a emp s o iden i y lis ed buildings in he da a and o emo e hese
om ou analysis o ene gy upg ade equi emen s. This p ocess is ou lined in
Appendix B, and leads o app oxima ely 5 pe cen o he RTB obse a ions being
iden i ied as o a p o ec ed na u e, hus educing he RTB sample om 209,035 o
196,305 dwellings. Gi en he special equi emen s o hese buildings in e ms o
8
Due o he p esence o ou lie s, mon hly en and loo a ea ha e been immed o he bo om and op pe cen iles.
Addi ionally, some obse a ions had missing in o ma ion in hese wo a iables. Fo hose obse a ions, he alues
we e impu ed. A logi model is an es ima ion p ocedu e ha uses a dis ibu ional o m ca e ing o bina y ou come
a iables. I d aws on he logis ic dis ibu ion.
34|Unde s anding in es men expendi u e needs
om he LASHU da a. The BER a ings p o ided in Table 4.1 ela e o he b oad A*,
B* and C* a ings. The majo i y o he upg ades a e o A* a ings (56 pe cen );
howe e , his is d i en p ima ily by p i a e p ope ies om he OSS da ase . The
LASHU da ase has mo e B* han A* a ings, while o AHBs he upg ades a e
equally spli be ween A* and B*.
TABLE 4.1 NUMBER OF OBSERVATIONS BY DATASET
Pos -upg ade BER a ing
Da ase
C*
B*
A*
To al
OSS AHB
0
355
350
705
OSS p i a e
0
36
327
363
Social housing
9
241
140
390
To al
9
632
817
1,458
Sou ce: LASHU and SEAI. Only obse a ions wi h non-missing cos o upg ade a e included.
In e ms o he ypes o p ope ies and hei geog aphic loca ions in hese da ase s,
some simple desc ip i e s a is ics a e p esen ed in Figu e 4.1. Th ee a iables a e
included: 1) a Dublin indica o cap u ing he p opo ion o p ope ies in Co. Dublin;
2) an apa men indica o cap u ing he p opo ion o p ope ies ha a e
apa men s; and 3) a la ge dwelling indica o , which gi es p opo ions o hose
p ope ies whose loo a ea is abo e 100m2. In all h ee cha s, he a e age alue
om he Census is p o ided o e e ence as he ho izon al line. In e ms o
loca ion, he LASHU da ase is close o bo h he RTB and SEAI PRS da abases, wi h
be ween 40 and 50 pe cen o he p ope ies in Dublin ac oss hese da ase s. In
con as , he AHB e o i s we e p ima ily conduc ed ou side o Dublin (less han
20 pe cen in Dublin) and only jus o e 30 pe cen o he OSS p i a e upg ades
we e in Dublin.
Rega ding p ope y ype and speci ically he sha e o apa men s, he LASHU
da ase is closes o he RTB and SEAI p i a e en al da ase s, wi h mo e han 40
pe cen o LASHU p ope ies being apa men s compa ed o 51 pe cen in he RTB
and SEAI samples. In con as , he OSS da a con ain mos ly houses a he han
apa men s. This is unsu p ising gi en ha he policy is a ge ed a homeowne s,
and houses a e likely o be easie o e o i on a e age han mul i-uni dwellings
wi h common a eas. The inal a iable p esen ed in Figu e 4.1 ela es o la ge
p ope ies (de ined as 100m2 o abo e). The OSS p i a e sample has a signi ican ly
highe sha e o la ge p ope ies, a nea ly 50 pe cen . This likely e lec s he sha e
o houses in he da a, which a e la ge han apa men s in gene al. The RTB has
he second highes sha e o la ge p ope ies while he SEAI, LASHU and AHB (OSS)
samples ha e a conside ably lowe sha e.

In es men equi emen s o ene gy e iciency upg ades in he en al sec o |35
FIGURE 4.1 KEY CHARACTERISTICS BY UPGRADE EXPENDITURES DATASET
Sou ce: RTB Regis a ions mic oda a, SEAI BER Resea ch da a.
In gene al, compa ing ac oss hese a iables, we ind ha he OSS da a o p i a e
homeowne s a e much mo e likely o conce n p ope ies ou side o Dublin and
la ge houses, while he LASHU da a in pa icula is mo e simila in he sha e o
p ope ies ha a e in Dublin and ha a e apa men s o he RTB and SEAI p i a e
en al sec o da a ou lined in Chap e 3. Figu e 4.2 p esen s h ee key da a ields
ac oss he a ious sub-samples and o he combined cos da a. The i s cha o
each o he sub-samples ela es o he p e- and pos -BER a ings dis ibu ions. This
is a c i ical piece o in o ma ion o ou esea ch as i plo s he obse ed changes
in ene gy e iciency o he p ope ies o which we ha e cos da a.
Focusing i s on he social housing upg ades da a, i is clea he majo i y o
p ope ies had a e y poo BER a ing be o e he upg ade, wi h he majo i y o
p ope ies ha ing an E o D a ing. Howe e , a e he upg ades mos p ope ies
had a B2, B1 o A3 a ing; his ep esen s qui e a signi ican inc ease. Fo he AHB
sample, he quali y o he housing s ock appea s o ha e been somewha be e as
he majo i y o p ope ies be o e he upg ade we e D1 o C a ed. A e he
upg ades, mos p ope ies we e B1 o A3 a ed. Fo he OSS p i a e sample, qui e
a uni o m dis ibu ion ac oss he a ings om C2 down is e iden be o e he
in e en ions. The e we e mo e G- and F- a ed p ope ies in his sample han in
he o he da ase s. Following he in e en ions, he as majo i y o he p ope ies
ha came h ough he OSS p i a e scheme had an A a ing, wi h app oxima ely 10
pe cen ha ing a B a ing. This ep esen s qui e a majo change in e ms o ene gy
e iciency. The inal panel in Figu e 4.2 includes he o e all sample, wi h he
majo i y o he p e-upg ade dis ibu ions popula ed by D-and C- a ed p ope ies
while he majo i y o he pos -wo k BER a ings we e B2 o A3 (nea ly 70 pe cen ).
The second igu e (middle column) p esen ed o each o he sub-samples is he
dis ibu ion o he numbe o BER changes. The OSS p i a e sample sees he la ges
jumps in e ms o he BER a ings, wi h many p ope ies mo ing up 7–12 places on
he BER scale. The OSS AHB da ase has mo e mode a e changes wi h, i e-poin
36|Unde s anding in es men expendi u e needs
inc eases being he mos equen jump. These mo e mode a e a ing
imp o emen s a e likely ela ed o he ela i ely be e s a ing poin o hese
p ope ies. The LASHU da a ha e a ai ly dispe sed ange o a ing imp o emen s
bu he mos equen ly occu ing ank jump in ol es nine- o en-scale place
inc eases. O e all, he mos equen ly obse ed inc eases a e i e o se en poin s,
e lec ing he size o he AHB sub-sample as a p opo ion o he o e all da ase .
The inal se o cha s ( igh -hand column) p esen ed in Figu e 4.2 show he
dis ibu ion o he in es men cos s associa ed wi h he ene gy e iciency
upg ades. While he majo i y o he da a p esen ed in he samples a e o
p ope ies eno a ed in 2022, he in es men cos da a ha e been ans o med o
2023 alues by de la ing he da a in line wi h he Cen al S a is ics O ice’s (CSO)
cos index o ma e ials and inpu s in o he cons uc ion sec o . Fo he LASHU
da a, he majo i y o he upg ades cos be ween €20k and €40k pe p ope y, bu
he dis ibu ion does ha e a long ail owa ds he highe alues wi h some la ge
expendi u es. Fo he AHB da a, he majo i y o he expendi u e is again be ween
€20k and €40k pe p ope y, wi h li le a ia ion. This likely e lec s he smalle
ange o BER ank inc eases seen in hese da a. Fo he OSS p i a e sample, he e
is a e y la ge sp ead in e ms o he expendi u e, and he a e age and median a e
much highe o his sample han o he o he s. This likely e lec s he di e ence
in housing ypes and he la ge houses on a e age, as well as he bigge a ings
inc eases (mo e A- a ed p ope ies a e he upg ades) han he o he da ase s. As
no ed p e iously, i is also possible ha local au ho i ies in pa icula may ha e
bene i ed om some economies o scale h ough bulk upg ades ac oss mul iple
p ope ies ha indi idual homeowne s in he OSS p i a e sample would no ha e
had. Finally, i canno be excluded ha some homeowne s in he OSS p i a e
sample may also ha e included cos s o some non-ene gy e iciency- ela ed
expendi u es incu ed when he wo ks we e ca ied ou .
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |37
FIGURE 4.2 SUMMARY OF BER DISTRIBUTIONS IN EXPENDITURE DATASETS
Sou ce: LASHU and SEAI One S op Sho da ase s.
No e: To al spen g aphs do no show ou lie alues abo e €100k.
4.3 ESTIMATION OF UPGRADE COST PER DWELLING
Ha ing p o iled he ene gy e iciency upg ade cos da ase s, ou nex aim is o
es ima e a dwelling-speci ic ene gy e iciency upg ade cos . To do so, we combine
he LASHU and OSS da ase s ou lined abo e, and ha monise hem o gi e a
consis en classi ica ion o dwelling ypes acco ding o Table A.1 (in he appendix).
In line wi h he na ional policy a ge o upg ading he housing s ock o a mid-B
BER le el, we use a sub-sample o obse a ions ha had upg ades o ei he B3, B2
38|Unde s anding in es men expendi u e needs
o B1, and had a p e-upg ade a ing o C o lowe . This lea es a eg ession sample
o 631 obse a ions, o which h ee-qua e s (481 obs.) a e upg ades o B1, while
123 a e upg ades o B2 and he emaining 27 a e upg ades o B3. The e o e, he
p edic ed cos s o upg ade cos s lean owa ds he highe end o he B a ing. The
eg ession sample con ains only 36 obse a ions om he p i a ely owned OSS
sub-sample, because he majo i y o hese upg ades we e o A* le el and a e
he e o e omi ed om ou es ima ion. The dependen a iable is always he log
o o al cos s o he upg ade in 2023 p ices. When esul s a e epo ed (e.g. in
Figu e 4.3), he loga i hmic alues a e con e ed back in o eu os. I is impo an o
no e ha he eg essions a e used o es ima e he a e age cos o he ene gy
e iciency upg ade. The ac ual cos s o indi idual p ope ies will de ia e om his
expec ed alue – some being lowe and some highe . Due o limi ed da a, bo h in
e ms o he sample size and obse able cha ac e is ics, he eg ession models
canno accoun o e e y possible de e minan o he upg ade cos s. Despi e he
di e si y o he housing s ock, when he p edic ed alues a e agg ega ed in Chap e
5, i is likely ha hese indi idual di e ences will end o coun e balance each
o he . Consequen ly, e en i indi idual es ima es di e ge, he means a e s ill
eliable es ima es o he agg ega e cos s.
Ou i s app oach is o es ima e he cos s as a se ies o dummy a iables, whe e
each dummy ep esen s one o he nine p e-upg ade BER a ings, om G o C1.
This app oach shown in equa ion Reg.1 is nume ically equi alen o es ima ing
a e age log cos s o e e y p e-upg ade BER a ing. The p edic ed cos s o each
dummy a e shown in Figu e 4.3, and he ull esul s a e in Table A.4 (appendix).
The es ima ed upg ade cos s ange om €40k o an F/G a ing, o a ound €26k o
a p e-wo ks BER o D o C.
𝑙𝑛𝐶𝑜𝑠𝑡𝑖= 𝛽0 + ∑𝛽𝑗(𝑃𝑟𝑒𝐵𝐸𝑅=𝑗)𝑖+𝜀𝑖
𝐶1
𝑗=𝐻
(Reg.1)
𝑙𝑛𝐶𝑜𝑠𝑡𝑖= 𝛽0 + ∑𝛽𝑗(𝑃𝑟𝑒𝐵𝐸𝑅=𝑗)𝑖
𝐶1
𝑗=𝐻
+𝜷𝑿𝑻+𝜀𝑖
(Reg.2)
In Equa ion Reg.2 addi ional con ol a iables, ep esen ed by ec o X, a e added
o he model. The h ee con ol a iables a e: a) a Dublin dummy, which akes he
alue 1 i a dwelling is loca ed in Coun y Dublin and 0 o he wise; b) an apa men
dummy which equals 1 i he dwelling is an apa men and 0 i he dwelling is a
house;
10
and c) a la ge-sized dummy which equals 1 i he dwelling is la ge han
10
See Table A.1 o de ails on dwelling ypes ac oss he da ase s.
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |39
100m2 and 0 o he wise. All hese dwelling cha ac e is ics a e also epo ed in he
RTB and SEAI da ase s, which allows us o p edic dwelling-speci ic cos s o he
upg ade be o e agg ega ing.
The esul s in Table A.4 show ha upg ades in Co. Dublin a e a ound 25 pe cen
mo e expensi e han elsewhe e and ha apa men upg ades a e 20–25 pe cen
cheape han houses. The dwellings abo e 100 m2 a e a ound 15 pe cen mo e
expensi e o e o i ; howe e , his ela ionship is s a is ically no as s ong. The
magni udes o hese ela ionships a e simila in all o he model speci ica ions.
Adding hese h ee a iables does no change he a e age upg ade cos s much,
hough hey signi ican ly imp o e he p edic i e powe o he model. In he
emaining ou speci ica ions, he ela ionship be ween he cos s and p e-upg ade
BER is modelled as a con inuous polynomial unc ion. The 𝑃𝑟𝑒𝐵𝐸𝑅 a ing is
con e ed in o a nume ical a iable 𝑃𝑟𝑒𝐵𝐸𝑅
 be ween 0 and 1, wi h an equally
spaced in e al be ween each p e-wo ks BER:
𝑃𝑟𝑒𝐵𝐸𝑅
=
{
0i 𝑃𝑟𝑒𝐵𝐸𝑅=𝐻
1/8 i 𝑃𝑟𝑒𝐵𝐸𝑅=𝐹
2/8 i 𝑃𝑟𝑒𝐵𝐸𝑅=𝐸2
⋮ ⋮
1i 𝑃𝑟𝑒𝐵𝐸𝑅=𝐶1
In Reg.3 he cos s a e modelled as a quad a ic unc ion and in Reg.4 as a cubic
unc ion o his a iable. This app oach elies on he assump ions ha nea by BER
a ings will in ol e simila upg ade cos s. This gi es be e p edic ions when he e
a e ela i ely ew obse a ions in he p e-upg ade a ing,
11
and educes he isk o
o e i ing.
𝑙𝑛𝐶𝑜𝑠𝑡𝑖= 𝛽0 + 𝛽1𝑃𝑟𝑒𝐵𝐸𝑅
𝑖 + 𝛽2𝑃𝑟𝑒𝐵𝐸𝑅
𝑖2
+𝜷𝑿𝑻+𝜀𝑖
(Reg.3)
𝑙𝑛𝐶𝑜𝑠𝑡𝑖= 𝛽0 + 𝛽1𝑃𝑟𝑒𝐵𝐸𝑅
𝑖 + 𝛽2𝑃𝑟𝑒𝐵𝐸𝑅
𝑖2
+𝛽3𝑃𝑟𝑒𝐵𝐸𝑅
𝑖3+𝜷𝑿𝑻+𝜀𝑖
(Reg.4)
Panels (3) and (4) in Figu e 4.3 show he es ima ed cos s, which a e b oadly in line
wi h esul s (1) and (2). The cos s a e highe o upg ades om G a €43.5k as well
as o upg ades om D and C, a abou €28k. Howe e , he cos s o F and E a e
sligh ly lowe han in eg ession models (1) and (2) a €32k–38k. To compa e he
models we calcula e he Akaike in o ma ion c i e ion (AIC) and he Bayesian
in o ma ion c i e ion (BIC), which a e s anda d measu es o he p edic i e powe
o he model. Bo h he AIC and BIC show he quad a ic and cubic equa ions ha e
be e p edic i e powe compa ed o he se ies-o -dummies app oach.
12
11
Fo example, he e a e only 33 obse a ions wi h a p e-upg ade BER a ing o E2.
12
No e ha o bo h AIC and BIC, lowe alues means be e p edic i e powe .

40|Unde s anding in es men expendi u e needs
The emaining wo eg ession models use equa ion Reg.3, bu a e es ima ed using
quan ile eg ession. Model (5) es ima es he expec ed median cos (50 h
pe cen ile). The p edic ed alues o he medians a e again simila o he p e ious
es ima es o he mean cos o upg ade. Finally, model (6) es ima es he 75 h
pe cen ile o he upg ade cos s as an uppe -bound es ima e o he upg ade cos s.
The i ed alues a e acco dingly highe and hey ange om €32.7k o €49.3k. This
scena io can be seen as a use ul uppe bound, which could occu unde a pe sis en
and ele a ed high cons uc ion in la ion en i onmen o excessi e capaci y
cons ain s in he cons uc ion sec o leading o p ice inc eases.
In se e al o he eg ession models, he es ima ed ela ionships a e no always
s ic ly dec easing. Fo example, based on Reg.2 he es ima ed mean upg ade cos s
om C1 a e €600 highe han an upg ade om he less ene gy e icien C2 BER
a ing. This is due o small numbe s o obse a ions in he da a, especially o C1
as only 12 dwellings in ou da a go an upg ade om C1 o B*. These non-
mono onic es ima es end o be ai ly small and o en no s a is ically signi ican .
The e o e, we do no make any u he unc ional- o m assump ions o use
nonlinea eg ession models o add ess his issue.
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |41
FIGURE 4.3 ESTIMATED AVERAGE COSTS OF UPGRADE BY PRE-UPGRADE BER IN €1K IN 2023 PRICES
Sou ce: LASHU and SEAI OSS da ase s.
No es: P edic ed alues a Dublin dummy=0.38, apa men =0.40, la ge=0.32. 95% con idence in e als. Full es ima ion
esul s able a e in Table A.4 (appendix).
4.4 TOWARDS AN AGGREGATE COST OF PRS DWELLING UPGRADES
The nex s ep in ou analysis is o de elop an agg ega e o e all cos o he PRS o
unde aking ene gy e iciency upg ades. To do his, we use he combina ion o
da ase s and es ima es o he cos s uc u es in he p e ious wo chap e s. Ou
gene al me hodology is as ollows: o each da ase ha measu es he BER p o ile
o he sec o (RTB and SEAI esea ch da abase), we ha e es ima ed a p ope y-
speci ic cos o each dwelling. We hen agg ega e his cos wi h he Census
42|Unde s anding in es men expendi u e needs
weigh s o ob ain a o al eno a ion cos . The es ima ion o he cos s ac oss
di e en me hodologies has been ou lined in he p e ious sec ion.
As men ioned abo e, o he pu poses o his analysis, we limi ou sel es o an
upg ade a ge in line wi h he Na ional Re o i Plan, which aims o B2 s anda d
dwellings as a key a ge . As discussed abo e, we he e o e use he cos da a in ou
es ima es o upg ade all p ope ies om hei cu en BER a ing o a minimum o
he a e age cos o a B* p ope y in ou da a. Gi en he as majo i y o ou B*
upg ade cos da a ela es o B1 o B2 p ope ies, ou upg ade scena io in essence
mo es all p ope ies o a mix o B2 o B1 le els. These cos s a e hen agg ega ed
ac oss all p ope ies ha a e cu en ly C a ed o below.
An illus a ion o ou agg ega ion p ocess can be seen in Table 4.2. The able d aws
on he RTB enancy egis a ions da a sample as desc ibed in Chap e 3. The
es ima es o cos o upg ade a e aken om he quad a ic i model (Reg.3)
desc ibed abo e. Because his model has he bes p edic i e powe i is used as a
benchma k model.
In Table 4.2, he es ima ed dis ibu ion o cu en BER a ings, adjus ed o non-
epo ed alues and Census weigh ed, is p esen ed in column (2). Column (3) is he
p opo ion o he o als excluding A/B and BER-exemp en al p ope ies.
Na u ally, o his scena io any p ope y ha is al eady ene gy e icien does no
equi e an upg ade and will no be included in u he calcula ions. Thus, he o al
numbe o p ope ies simula ed o upg ade is 242,467.
In column (5), he a e age cos o he upg ade pe p ope y is p o ided o each o
he BER g oups. Fo example, he a e age upg ade cos s o B* o all p ope ies is
jus o e €30k bu i anges ac oss he s a ing BER; he a e age upg ade cos o
G- a ed p ope ies is €43k and his declines o €28k o C- a ed p ope ies. The
o al cos o each g oup is p esen ed in column (6) wi h he p opo ion o he o al
cos in column (8). In his scena io, he o al sec o upg ade cos s a e
app oxima ely €7.3bn. Jus unde €5bn o his o al ela es o p ope ies ha a e
cu en ly D o C a ed. Al hough he a e age cos is lowe han o low-e icien
G/F/E a ed p ope ies, he e a e many mo e mid-e icien p ope ies o e all.
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |43
TABLE 4.2 EXAMPLE USING RTB DATA AND QUADRATIC FIT UPGRADE COSTS
Dwellings
Upg ade cos
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Cu en BER
Es .
numbe
%
Cum. %
Mean
(€1,000s)
To al
(€mil)
Cum.
(€mil)
To al (%)
Cum. (%)
G
9,663
4%
4%
43.5
421
421
6%
6%
F
9,523
4%
8%
38.9
371
791
5%
11%
E2
10,239
4%
12%
34.8
357
1,148
5%
16%
E1
16,005
7%
19%
31.7
507
1,655
7%
23%
D2
31,136
13%
32%
29.9
932
2,587
13%
35%
D1
40,708
17%
48%
28.9
1,176
3,763
16%
51%
C3
45,029
19%
67%
28.3
1,274
5,037
17%
69%
C2
38,859
16%
83%
28.3
1,100
6,137
15%
84%
C1
41,306
17%
100%
28.9
1,194
7,331
16%
100%
Fo upg ade
242,467
100%
100%
30.2
7,331
7,331
100%
100%
B*
46,145
Dwellings no included in upg ade cos calcula ions.
A*
23,925
Exemp
18,095
To al
330,632
No es: Columns 2–4 numbe o dwellings based on RTB da a, adjus ed o missing BER a ings, popula ion-weigh ed wi h
numbe o dwellings om he Census, and excluding dwellings in p o ec ed supe s uc u es ( ow ‘Exemp ’).
Column (5) shows a e age cos s in €1k in 2023 p ice le els, using equa ion Reg.3 model and combined da a om
LASHU and he SEAI OSS se ice. These a e ages accoun o dwelling cha ac e is ics (apa men , Dublin-based,
la ge-size dummies), and he e o e di e sligh ly om ep esen a ion in Figu e 3.3 whe e hese cha ac e is ics a e
held cons an ac oss all p e-upg ade BER a ings.
Figu es in columns (6) and (7) a e in million eu os.
Table 4.3 p esen s he ange o agg ega e es ima es calcula ed ac oss all six models
es ed in he p e ious chap e s and ac oss bo h housing s ock da ase s, he RTB
and SEAI da a. In all igu es and cha s, he cos da a a e p o ided in 2023 p ices
and assume upg ade echnologies and associa ed in es men s cos s in line wi h
hose in he mic o da ase s abo e. The mos pa simonious cos equa ion
speci ica ion, which does no con ain any con ol a iables, gi es a o al upg ade
cos in he RTB da a o €6.9bn while he upg ade cos in he SEAI da ase is €7.7bn.
When con ols ( loo a ea, Dublin and dwelling ype) a e included, he cos s
inc ease o €7.3bn and €7.85bn using he RTB and SEAI da ase s espec i ely.
50|In es men ba ie s and he landlo d s uc u e
In e ms o he age p o ile
14
o landlo ds (Figu e 6.3), he e is a g ea e sha e o
esiden ial landlo ds o e he age o 45 (68 pe cen ) han non-landlo ds (61 pe
cen ).
15
To p o ide a mo e g anula spli o he da a, he e a e mo e landlo ds aged
be ween 46 and 65 yea s (53 pe cen ) han non-landlo ds (36 pe cen ).
FIGURE 5.3 COMPARING LANDLORDS AND NON-LANDLORDS: AGE
Sou ce: Analysis based on CSO HFCS da a.
Da a p o ided by he CSO om hei Ren al ma ke in I eland 2021 epo show
ha many o he olde landlo ds ha e mul iple p ope ies (Figu e 5.4). The age o
he landlo d may impac hei decisions a ound in es men expendi u e on ene gy
e iciency. Fo example, bank c edi access may ge mo e di icul wi h age, and on
e i emen , as c edi is a ioned h ough a sho e loan e m being a ailable and
less income o co e epaymen s; i.e. he households a e in ha pe iod o hei
li ecycle in which hey a e unning down accumula ed inancial asse s. Olde
landlo ds may ha e a sho e in es men ho izon o holding he asse , which may
a ec he ne p esen alue o any in es men . The assessmen is likely o depend
on he cos o he in es men , he a ailabili y o g an s o subsidies (which is
ou side he scope o his epo ) and exis ing BER. I is also possible ha he abili y
o e-p ice he en h ough he Ren P essu e Zone legisla ion a e ene gy
e iciency upg ades would incen i ise hem o make he expendi u e. The deg ee
o compliance and moni o ing in ol ed could also ac o in hei decision-making
p ocess.
14
Ages p esen ed e e o heads o household.
15
Ages p esen ed e e o heads o household.
15
Ages p esen ed e e o heads o household.
32 26 27
15
39
20 17
25
0
10
20
30
40
18-45 46-55 56-65 66 plus
Landlo ds
Non-landlo ds

In es men equi emen s o ene gy e iciency upg ades in he en al sec o |51
FIGURE 5.4 LANDLORD AGE BY NUMBER OF TENANCIES
Sou ce: CSO da a based on RTB enancy egis a ion da a.
No es: The RTB da a only ela e o new enancies and Pa 4 enewals as he RTB did no collec annual egis a ions o
he pe iod which he CSO unde ook he analysis.
Finally, we compa e he employmen s a us o landlo ds and non-landlo ds.
Employmen s a us is spli in o h ee g oups; wo king, e i ee o ‘o he ’, whe e
o he e e s o hose who a e unemployed, on empo a y lea e, s uden s, in he
mili a y, ul illing domes ic se ices, o pe manen ly disabled (Figu e 6.5). Almos
one-qua e (23 pe cen ) o bo h landlo ds and non-landlo ds we e ound o be
e i ed. Mo e landlo ds wo ked (64 pe cen ) han non-landlo ds (53 pe cen ).
Nea ly double he sha e o non-landlo ds we e in he o he ca ego y compa ed o
landlo ds, a 25 and 13 pe cen espec i ely.
11 1 1
26
11 5
24
53
55
46
53
20 34 49
22
0
20
40
60
80
100
1–2 enancies 3–19 enancies 20+ enancies All landlo ds
Landlo ds wi h
Pe cen
Landlo d age
65 yea s and o e
45–64 yea s
30–44 yea s
0–29 yea s
Numbe o enan s landlo d has
52|In es men ba ie s and he landlo d s uc u e
FIGURE 5.5 COMPARING LANDLORDS AND NON-LANDLORDS: EMPLOYMENT
Sou ce: Analysis based on CSO HFCS da a.
5.1.3 O e iew o income and weal h
One impo an aspec o in es men capaci y is income. We examine bo h landlo d
employmen income and o al ea nings – i.e. including income om s ocks and
bonds, pensions, en , social ans e s and o he sou ces o household income.
Table 5.1 below con ains he weigh ed a e age and median le el o income o
landlo ds om he HFCS da ase o he yea 2020. The a e age o al household
income o landlo ds was app oxima ely €110,816. The median household income
o landlo ds was €92,400.
TABLE 5.1 SUMMARY OF LANDLORD ANNUAL HOUSEHOLD INCOME DATA – SURVEY YEAR 2020
Mean
Median
Landlo ds
€110,816
€92,400
Sou ce: Analysis based on CSO HFCS da a.
Ano he use ul aspec o conside in his con ex is he numbe o p ope ies
owned by landlo ds ac oss he income dis ibu ion. These a e p esen ed in Figu e
5.6, which d aws on he CSO’s Ren al ma ke in I eland 2021 epo .
64
13 23
53
25 23
0
20
40
60
80
Wo king O he Re i ee
Landlo ds
Non-landlo ds
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |53
FIGURE 5.6 INCOME DISTRIBUTION OF RTB LANDLORDS BY LANDLORD SIZE
Sou ce: CSO da a based on RTB enancy egis a ions da a.
These da a show ha landlo ds wi h mo e han 20 enancies ha e conside ably
highe incomes han hose wi h ewe enancies. This again highligh s he po en ial
inancial capaci y challenge o lowe income, single o ‘ ew p ope y’ landlo ds.
I is impo an o no e he di e ence in income dis ibu ion ac oss he CSO da ase
and he HFCS da a. As he CSO only include ea ned income da a, hey likely do no
cap u e income om weal h ha could enhance he income posi ion o landlo ds.
Fu he mo e, hey only conside new ac i e enancies as pe he RTB da ase ;
he e o e he da a a e biased owa ds only conside ing hose enancies ha u n
o e mo e equen ly. The ESRI/RTB Ren Index (as well as o he ma ke moni o ing
epo s such as Da .ie) show alling u no e in he ma ke , meaning ha new
enancies a e becoming less ep esen a i e o he en i e ma ke o e ime. Fo
example, in 2007–2008, o e 100,000 new enancies we e egis e ed e e y yea
wi h he RTB. This had d opped o 64,000 in 2021.
Ano he c i ical ac o in he abili y o landlo ds o in es in hei p ope ies is he
annual e u n hey ecei e in en . These cash lows a e he yield ha could be
used o o se and co e any in es men expendi u es, and a e he e o e a c i ical
componen o any assessmen o in es men easibili y. Table 5.2 p esen s he
mean and median landlo d income om en al p ope ies o he yea s 2018 and
2020. The a e age en al income in 2020 was app oxima ely €20k pe annum,
while he median was €14.4k. These had inc eased by 14 pe cen and 4 pe cen
espec i ely o e his pe iod.
0
10
20
30
40
50
Pe cen o landlo ds
All landlo ds
Landlo ds wi h
1–2 enancies
Landlo ds wi h
3–19 enancies
Landlo ds wi h
20+ enancies
54|In es men ba ie s and he landlo d s uc u e
TABLE 5.2 SUMMARY OF LANDLORD ANNUAL RENTAL INCOME DATA – SURVEY YEARS 2018/2020
Su ey yea
Mean
Median
2018
17,627
13,800
2020
20,243
14,400
% Di e ence
+14%
+4%
Sou ce: Analysis based on CSO HFCS da a.
Gi en ha landlo ds o esiden ial p ope ies may combine ea ned and non-ea ned
income when conside ing hei le ing, i is use ul o conside wha p opo ion o
hei o al income comes om he en al p ope y. An examina ion o en al
income shows ha he mean sha e o en al income as a p opo ion o o al
household income o landlo ds was 24 pe cen , i.e. on a e age, en al income
makes up app oxima ely one-qua e o landlo ds’ income. Figu e 5.7 p esen s he
p opo ion o landlo ds agains he sha e o hei income ha comes om en al
income. A majo i y o landlo ds ea ned less han 20 pe cen o hei income om
en al sou ces.
FIGURE 5.7 PROPORTION OF LANDLORDS BY RENTAL INCOME AS % TOTAL INCOME
Sou ce: Analysis based on CSO HFCS da a.
A key ac o in e ms o he o e all abili y o landlo ds o in es in ene gy e iciency
echnology is he le el o weal h ha hey hold. This in pa icula ela es o
inancial weal h o deposi s ha can be easily deployed o capi al in es men
pu poses. In his sec ion, we d aw on he da a om he HFCS on weal h s uc u es
o p o ide a summa y o e iew o he esou ces a ailable o landlo ds.
In he HFCS, a numbe o weal h a iables can be de i ed. Table 5.3 below p esen s
he de ini ions o he weal h a iables ha we use in he analysis.
65 59
19 21
17 20
0
20
40
60
80
100
2018 su ey 2020 su ey
% o o al household income
O e 40%
20%–40%
Below 20%
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |55
TABLE 5.3 DEFINITIONS OF WEALTH VARIABLES
Va iable
De ini ion
Real asse s
Collec i e alue o household main esidence, p ope ies, ehicles, sel -employmen
businesses and aluables.
Financial asse s
Collec i e alue o any s ocks, bonds, mu ual unds, sa ings accoun s, managed
accoun s, non-sel -employmen businesses, sigh accoun s, p i a e lending, olun a y
pensions and ‘o he ’ asse s.
To al asse s
Value o eal asse s and inancial asse s.
Ne weal h
To al asse s – o al ou s anding balance o a household’s liabili ies.
Table 5.4 p esen s he summa y s a is ics o he main weal h a iables o
landlo ds. Median o al asse s o landlo ds we e jus o e €528k, wi h a median
ne weal h o €392.5k. Howe e , his ela es o weal h om he p inciple p i a e
esidence as well as in es men s. In e ms o inancial asse s, he median was
€31.5k.
TABLE 5.4 SUMMARY OF MAIN WEALTH VARIABLES
Landlo d
Va iable
Mean (€)
Median (€)
To al asse s
922,800
528,094
Ne weal h
867,757
392,519
Real asse s
772,685
416,100
Financial asse s
83,834
31,551
Sou ce: Analysis based on CSO HFCS da a.
One impo an aspec o conside when hinking abou he issue o household
in es men is he le el o sa ings. Sa ings a e included abo e in he ‘ inancial
asse s’ ca ego y. Howe e , due o liquidi y, i is use ul o conside hem sepa a ely.
The da a a e p esen ed below in Table 5.5.

56|In es men ba ie s and he landlo d s uc u e
TABLE 5.5 SUMMARY OF SAVINGS (€)
Mean (€)
Median (€)
Landlo d
37,148
17,000
Non-landlo ds
19,746
6,460
To al
21,008
7,000
Sou ce: Analysis based on CSO HFCS da a.
The mean le el o sa ings o landlo d households is €37k and he median is €17k.
This sugges s ha many landlo ds would ha e o ob ain c edi in o de o in es
subs an ially in hei eal es a e as he in es men cos s o hei en al p ope ies
would likely be much mo e han his, based on he cos s p esen ed in Chap e 4.
In addi ion o hei weal h, landlo ds a e o en also ca ying conside able deb s,
ela ing o hei in es men bo owings bu also hei own esiden ial dwelling. The
da a in Table 5.6 p esen he le el o deb and he cu en loan- o- alue a ios o
landlo ds. The mean liabili ies ca ied by landlo ds we e jus o e €200k in 2020,
wi h a median o €104k.
TABLE 5.6 SUMMARY OF INDEBTEDNESS
Mean
Median
Summa y o o al ou s anding Balance on household liabili ies (€)
201,138
104,032
Po olio loan- o- alue a io
37.8%
33.1%
Sou ce: Analysis based on CSO HFCS da a.
Gi en ha i seems a dis inc possibili y ha many landlo ds would equi e
colla e alised c edi o inance in es men in o imp o emen s in hei p ope ies,
cu en loan- o- alue a ios a e examined o explo e he ex en o colla e al
a ailable in he p ope ies. These loan- o- alue a ios a e calcula ed a he
po olio le el, including he main esiden ial dwelling, and include all mo gage
loans in he nume a o . The mean and median igu es a e p esen ed in Table 5.6.
These landlo ds ha e a mean loan- o- alue a io o 38 pe cen . This ep esen s he
amoun o ou s anding deb on p ope ies as a p opo ion o he alue o hose
p ope ies. The e o e, on a e age, landlo ds ha e ou s anding deb wo h less
han 40 pe cen o he alue o hei p ope ies. Hence, i is possible ha some
landlo ds would be able o a ain inancing o imp o emen s. This would depend,
howe e , on he le el o in es men equi ed, and he e m o he loan ha would
be a ailable o he bo owe (likely linked o hei age). The issue o inancial
capaci y is discussed below.
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |57
5.2 SIMULATING HYPOTHETICAL FINANCING GAPS
Ha ing e iewed he income and weal h posi ion o I eland’s household landlo ds,
he aim o his sec ion is o ge a clea e assessmen o he abili y, and willingness,
o landlo ds o unde ake in es men s in hei p ope ies. In he i s exe cise we
unde ake, we use he in o ma ion on weal h om he HFCS su ey and combine
his wi h hypo he ical in es men expendi u es, calib a ed om he da a in
Chap e 4, o simula e in he mic oda a he ex en o which households could co e
his expendi u e h ough hei own inancing esou ces. Second, o hose
households ha ha e insu icien esou ces a ailable, we hen explo e hei abili y
o inance and co e hose expendi u es using comme cial inancing. This can
p o ide insigh in o he abili y o households o b idge he gap. I mus be no ed
ha his esea ch does no conside he suppo a ailable om exis ing policy
mechanisms, which would na u ally be a ailable o aid homeowne s and landlo ds.
In an a emp o p o ide some insigh in o he ex en o which I ish landlo ds ha e
he inancial capaci y o in es in ene gy e iciency upg ades, we deploy some
simple hypo he ical in es men scena ios and explo e he numbe o households
ha could unde ake hese in es men s, based on hei indi idual le el o weal h
and esou ces as ou lined in he HFCS su ey.
These scena ios a e de eloped o be hypo he ical in na u e bu o e lec he ange
o in es men s ha could be needed o mo e s aigh o wa d in es men s, such
as a ic insula ion, and ins alla ion o hea pumps owa ds mo e comple e
e o i s. Fo simplici y, we use ou po en ial in es men expendi u es o €25k,
€50k, €75k and €100k, and simula e how many household landlo ds could a o d
o co e hese expendi u es using hei inancial asse s o deposi s in he HFCS
mic o da a.
In each o he hypo he ical scena ios, we de e mine a inancial gap, which is he
di e ence be ween he le el o he in es men and he le el o esou ces ha he
household has a hei disposal:
In es men gap = The hypo he ical in es men le el – To al household
inancial weal h (o deposi s)
Some households will ha e su icien esou ces o co e he expendi u e;
he e o e, hey will no ha e a gap pe se, i.e. he abo e indica o will be nega i e.
We can he e o e de ine an indica o a iable ha akes he alue o 1 i a
household has insu icien esou ces o co e he expendi u e and is 0 o he wise:
he in es men gap o each J in es men alue o each household h as ollows:
𝐼(𝐺𝐴𝑃)∗𝑗,ℎ={𝐼∗𝑖𝑓 𝐼(𝐺𝐴𝑃)𝑗>0
0𝑖𝑓 𝐼(𝐺𝐴𝑃)𝑗≤0
58|In es men ba ie s and he landlo d s uc u e
whe e: 𝐼(𝐺𝐴𝑃)𝑗= 𝐽−𝐹ℎ
𝐽 akes he alues o each o he abo e in es men s and 𝐹ℎ is he measu e o
household ℎ inancial weal h used in he scena io. We will he e o e epo wo
ou comes om his analysis in ou epo ing:
• sha e o households wi h an in es men gap (% o landlo ds); and
• he median le el o he gap o each household wi h a gap.
Typically, in es men is inanced by liquid unds o sa ings held in accoun s (i own
unds a e being used). The e o e, we use wo di e en measu es o inancial asse s
in his analysis. Fi s , we use o al inancial asse s, as de ined in Sec ion 5.1.
Howe e , we also use o al deposi s as a mo e ealis ic indica o o inancial
esou ces a ailable o in es , as some landlo ds may hold o he inancial asse s in
longe e m, mo e illiquid holdings, which hey may no be able o access (such as
pension unds). We p esen indings o he ou hypo he ical in es men le els as
well as he wo di e en measu es o inancial weal h.
Table 5.7 p esen s he p opo ion o landlo ds who a e expe iencing an in es men
gap, i.e. he p opo ion o landlo ds who a e unable o co e he in es men
expendi u es using hei inancial weal h. The ou in es men simula ions a e
p esen ed in each ow o he able, while he wo di e en measu es o he
inancial esou ces a e p esen ed in he columns. As deposi s a e included in
inancial weal h, he s ic es scena io in e ms o di icul y o achie e o landlo ds
is he high in es men (€100k) inanced by deposi s.
TABLE 5.7 PROPORTION OF LANDLORDS UNABLE TO FINANCE THE INVESTMENT ACTIVITY BY
FINANCIAL MEASURE AND INVESTMENT SIZE
Simula ed in es men equi emen
F = Financial weal h
F = Deposi s
J=€25k
49%
49%
J=€50k
62%
70%
J=€75k
71%
81%
J=€100k
77%
86%
Sou ce: Analysis based on CSO HFCS da a.
The esul s o he p opo ion o landlo ds unable o co e he indica i e
hypo he ical in es men alues using hei inancial weal h/deposi s a e p esen ed
in Table 5.7. In o al, jus unde hal o I ish landlo ds would ha e insu icien
sa ings o a o d a €25k in es men using ei he hei o al inancial weal h o hei
sa ings. This ises o 62 pe cen (70 pe cen ) when conside ing he use o inancial
weal h (deposi s) o co e he €50k in es men . Conside ing he la ges
hypo he ical in es men o €100k, 23 pe cen o landlo ds would ha e su icien
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |59
o e all inancial weal h o co e he expendi u e equi ed o his le el o
in es men , while 14 pe cen o landlo ds would ha e su icien deposi s o co e
his expendi u e. These igu es poin o qui e a complex pic u e in e ms o he
in es men capaci y o landlo ds. Nea ly hal o landlo ds a e unable o co e he
smalles in es men le el conside ed, o €25k, poin ing o a no able absence o
in es men capi al and a low liquid weal h concen a ion o mos landlo ds. As
hese landlo ds a e unlikely o be willing o in es all o hei unds on ene gy
e iciency, ac i a ing e o i s on hei p ope ies is going o be challenging wi hou
suppo s. On he o he hand, be ween one-in- i e and one-in-se en ha e su icien
weal h o co e la ge in es men s o €100k, depending on whe he o al inancial
weal h o deposi s a e included.
To unde s and mo e abou he in es men gap aced by hose landlo ds wi h
insu icien unds, Table 5.8 p esen s he median in es men gap o hose
landlo ds, i.e he gap be ween hei own inancial asse s/deposi s and he
hypo he ical in es men s. Again, he in o ma ion is p esen ed in e ms o median
le els o p o ide in o ma ion on he ypical gap.
TABLE 5.8 MEDIAN INVESTMENT GAP OF LANDLORDS UNABLE TO FINANCE THE INVESTMENT
ACTIVITY BY FINANCIAL MEASURE AND INVESTMENT SIZE
Simula ed in es men equi emen
F = Financial weal h
F = Deposi s
J=€25k
13,966
14,161
J=€50k
32,130
32,384
J=€75k
51,067
51,067
J=€100k
71,197
73,266
Sou ce: Analysis based on CSO HFCS da a.
The ypical gap o hose landlo ds wi h insu icien unds o co e he €25k
in es men expendi u e is app oxima ely €14k o nea ly hal o he in es men
cos s. This ises o €32k o he €50k in es men , and €50k o he €75k in es men
using bo h measu es. The ypical gap o hose landlo ds wi h insu icien unds o
co e he €100k in es men is €73k.
5.2.1 Cos o inancing
The inal elemen in his sec ion explo es he cos implica ions o landlo ds i hey
we e o bo ow he inancing o he in es men gap. To do his, we unde ake he
ollowing simula ion. Fo each landlo d who has a inancing gap as de ined abo e,
we simula e he cos o he o e all in es men gap, in e ms o he mon hly
epaymen s i he loan we e o be inanced using a pe sonal loan. We use an
66|In es men ba ie s and he landlo d s uc u e
Howe e , hese indica o s a e unlikely o cap u e he ull ex en o landlo ds wi h
in es men equi emen s in hei own household.
FIGURE 5.11 PERCENTAGE OF HOUSEHOLDS INDICATING A PROBLEM IN THEIR DWELLING
P ope y is da k/lacks ligh
P ope y has a p oblem wi h damp, leaking oo o o
Sou ce: Analysis based on EU SILC da a.
To gain u he insigh s in o he isk appe i e o in es men among landlo ds, we
can d aw on he HFCS da a. An indica o in he su ey comes in he ques ion ha
asks households o iden i y hei willingness o ake isks o ea n ewa ds. This
indica o is use ul in ha i may p o ide a p oxy o landlo ds’ willingness o engage
in ene gy e iciency expendi u es wi h an unce ain e u n. Households a e asked
o indica e whe he hey a e willing o ake abo e-a e age isk o ea n abo e-
a e age e u n, a e age isk o ea n a e age e u n o no isk a all. This is a good
indica o o in es men appe i e. We p esen he da a below in Figu e 5.12 o
hose landlo ds who a e iden i ied as ha ing an in es men gap in he €25k
scena io. App oxima ely 41 pe cen indica e hey would be unwilling o ake any
isk, a u he 37 pe cen would ake a e age isk and only 29 pe cen would ake
abo e-a e age isk. This sugges s qui e a isk a e se g oup o households.
5
3
6
9
0 5 10 15
Non-landlo d
Landlo d
Pe cen o households
I eland
O he
coun ies
13
10
12
9
0 5 10 15
Non-landlo d
Landlo d
Pe cen o households
I eland
O he
coun ies

In es men equi emen s o ene gy e iciency upg ades in he en al sec o |67
FIGURE 5.12 PROPORTION OF LANDLORDS WITH AN INVESTMENT GAP BY THEIR WILLINGNESS TO INVEST
Sou ce: Analysis based on CSO HFCS da a.
No e: Based on a scena io wi h an in es men equi emen o €25k.
While we ha e aised he issue o landlo d age as a po en ial discou aging ac o in
e ms o in es men p opensi y, a numbe o gene al b oade ac o s may
coun e ac his. F om a pu ely inancial pe spec i e, any in es men should be
e alua ed on a alue o money basis, depending on he speci ic in e nal a e o
e u n on he in es men using discoun ed cash lows. This would inco po a e an
assessmen o he in es men cos s, he cos o inancing i equi ed, he egula o y
en i onmen and he u u e en al p ice. As en s can be ese unde he Ren
P essu e Zone legisla ion once a majo change in ene gy e iciency is secu ed, any
landlo d is likely o be able o e-p ice o eco e he cos o he in es men in
inancial e ms. They can also a ail o g an suppo s. These ac o s would likely
o se some o he hesi ancy in e ms o isk appe i e no ed he e. The impac o
he pe cei ed and ac ual le el o compliance moni o ing would also ac o in o
hei conside a ions. The complex in e play o all hese ac o s is likely o
de e mine he ac ual obse ed in es men le els ha will occu .
29.7%
37.2%
41.1% Abo e a e age isk
A e age isk
No isk
68| Concluding ema ks
CHAPTER 6
Concluding ema ks
Re o i ing he I ish p i a e en al sec o (PRS) o educe ca bon emissions will be
a c i ical componen o he o e all clima e ansi ion s a egy o he esiden ial
housing sec o . Howe e , he e a e a numbe o no able challenges wi h he en al
sec o ha aise he complexi y o mee ing hese a ge s ela i e o he e o i
ac i i y o homeowne s. In pa icula , he exis ence o spli incen i es be ween
landlo d and enan , as well as issues ega ding he capaci y o he landlo d sec o
o inance and deploy he capi al in es men s equi ed, a e dis inc challenges,
which di e om hose p esen ing in o he esiden ial ma ke coho s.
One s a ing poin o measu ing he scale o his challenge can be ound in
empi ical es ima es o he in es men cos s equi ed o upg ade he sec o , based
on de ailed in o ma ion on he cu en ene gy e iciency s a us o en al
p ope ies. In his epo , we aim o begin his p ocess by achie ing h ee main
objec i es. Fi s ly, we p o ide a p o ile o he sec o in e ms o i s measu ed
ene gy e iciency (ac oss building ene gy a ing (BER) ca ego ies). Secondly, we
p o ide cos es ima es (bo h a a p ope y le el and an agg ega e le el) as o wha
he in es men cos would be o upg ade he housing s ock conce ned o a highe
ene gy e iciency le el. Thi dly, we explo e he possible ba ie s o in es men
ac i i y ha may exis due o he high sha e o ‘household landlo ds’, and hei
po en ially limi ed inancial capaci y o in es in ene gy e iciency upg ades.
In doing so, we d aw on a numbe o mic o-le el da ase s. We used he Residen ial
Tenancies Boa d (RTB) egis e o annual enancies and he Sus ainable Ene gy
Au ho i y o I eland (SEAI) BER a ings da abase o p oxy he s ock o p ope ies in
he en al sec o . Alongside his, we used Local Au ho i y Social Housing Upg ade
(LASHU) da a and he SEAI One S op Shop (OSS) in o ma ion o cos ene gy
e iciency upg ades. A inal sou ce was he Household Finance and Consump ion
Su ey (HFCS).
6.1 FINDINGS ON INVESTMENT NEEDS
Using a se ies o da ase s ( he RTB da a o 2022, and SEAI da a o a ious pooled
yea s), we es ima e ha app oxima ely 80 o 85 pe cen o p i a e en ed
dwellings cu en ly ha e a BER a ing below B; his cons i u es app oxima ely
240,000 o 260,000 p ope ies. The as majo i y o p ope ies in his g oup ha e
a D o C a ing. In e ms o he indi idual cos o upg ading p ope ies o a B a e age
a ing, he da a indica e an a e age cos o €43k o G- a ed p ope ies, and jus
unde €30k pe p ope y o hose cu en ly C a ed. Ou es ima ed agg ega e cos
o he equi ed upg ades in he sec o anges be ween €7bn and €8bn in o al.
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |69
These igu es sugges a subs an ial in es men equi emen in o de o he sec o
o mee he p oposed B2 a ings o p ope ies ha a e en ed in he p i a e
ma ke . The achie emen o his aim will be dependen on a mul i ude o ac o s,
including he s uc u e o he landlo d sec o (indi iduals e sus co po a ions), he
a ailabili y o policy suppo s, he egula o y en i onmen and landlo d
cha ac e is ics (e.g. li ecycle s age, access o inance, in es men appe i e). Fo
example, co po a e landlo ds a e mo e likely o be able o use economies o scale
and ha e su icien inancial esou ces o upg ade p ope ies ela i e o household
landlo ds. The in es men decision will howe e be assessed in he con ex o a
changing sec o , wi h unce ain e u n p o iles gi en he exis ence o en
con ols.
17
The es ima es p o ided in his esea ch epo con ibu e o he
eme gence o a clea e pic u e ega ding he scale o his challenge.
A numbe o ca ea s and limi a ions o ou esea ch mus be kep in mind. Fi s ,
we a e wo king wi h mainly sel - epo ed BER a ings; biases may exis in which
case he ac ual BER dis ibu ion may di e om ha ou lined he e. We do use he
SEAI da ase as a seconda y sou ce o checking he obus ness o he indings;
none heless we canno ule ou his possibili y en i ely. Second, he sample o cos
upg ades we use is ela i ely limi ed in e ms o he numbe o obse a ions and
he ocus on local au ho i y ac i i ies. Again, his may lead o biases in ou cos
es ima es. Howe e , no o he da a sou ce is a ailable o he pu poses o his
s udy. Fu u e esea ch ha add esses hese da a gaps would be welcome.
A u he issue ela es o ou me hods used o ma ch he cos es ima es wi h he
eco ded change in BER a ings. As BER a ings a e e y dependen on he speci ic
p ope ies conce ned, he de elopmen o a mo e de ailed ma ching sys em, a
he p ope y le el, a he han he use o a pa simonious me hod wi h a small
numbe o cha ac e is ics would ma k a u u e imp o emen . Finally, he
deploymen o addi ional scena ios (such as o an A- a ed le el) would be wo hy
o conside a ion, hough hey would be likely o aise he cos .
6.2 FINDINGS ON THE LANDLORD STRUCTURE AND FINANCIAL
CAPACITY TO INVEST
The indings o his esea ch p esen a conside able challenge o he achie emen
o deca bonisa ion h ough in es men s in e o i o he household landlo d
sec o . In e na ional esea ch has indica ed ha sub-op imal in es men s in
ene gy e iciency in he en al sec o occu qui e equen ly due o ma ke ailu es
and he spli incen i es in cos and yield on in es men .
17
I is impo an o no e ha exemp ions a e in place o subs an ial eno a ions, including ene gy e iciency upg ades,
al hough e y ew ha e been eco ded (Co ey e al., 2022).
70| Concluding ema ks
The e a e easons o belie e ha hese ac o s a e likely o be p esen o many
I ish household landlo ds, wi h conside able ba ie s p esen ing o hei in es ing
in e o i . Many landlo ds do no ha e he inancial esou ces o make he
simula ed in es men s we deploy in his analysis when elying on hei exis ing
inancial asse s. Fu he mo e, he e a e likely o be bo h demand and supply side
ac o s ha ac o inhibi in es men in e o i in such p ope ies. On he demand
side, some landlo ds may ace inancial challenges in hei gene al pe sonal
ci cums ances: one in eigh landlo ds has less han €50k o al annual income.
These landlo ds a e unlikely o be in a posi ion o commi cash lows owa ds
in es men , especially i he e u n on ha in es men is no bo ne by hem.
Secondly, many o hese households a e likely o ha e o also conside he e o i
o hei main esiden ial dwelling, which is ano he call on hei esou ces in an
ene gy e iciency con ex . F om he supply side, access o c edi in epaymen
e ms could be challenging o hese households i comme cial inance is needed
o co e he inancing gaps, pa icula ly gi en he olde age p o ile o hese
landlo ds, a ac o ha educes any po en ial payback pe iod. Rising in e es a es
by global cen al banks, as pa o he snapback in mone a y policy due o high
in la ion, is also likely o aise he cos o inancing in es men in ene gy e iciency,
o p esen iabili y challenges, and o u he enhance spli incen i e issues. Low
cos loans, such as he SBCI-backed Home Ene gy Loan Scheme, can be impac ul
in e ms o lowe ing he cos o inancing in es men s ha come unde i s emi .
This epo ocuses on illing a knowledge gap in e ms o he cu en ene gy-
e iciency p o ile o he PRS, likely upg ade cos s and household landlo ds’ inancial
posi ion. I s indings sugges ha policymake s ace a e y complex challenge in
ela ion o encou aging hese landlo ds o engage in ene gy e iciency upg ades on
hei en ed p ope ies.
I is beyond he s udy’s scope o assess he nume ous policies, g an s and suppo s
ha a e cu en ly a ailable, and hei po en ial ole in add essing such inancing
gaps, as well as any issues ega ding he willingness o landlo ds o add ess he
challenges iden i ied. These a e c ucial opics o u u e esea ch.
The ocus o his epo has been on he cu en s a e o play in e ms o ene gy
e iciency and landlo ds’ inancial si ua ion, and incen i es. Howe e , he majo
implica ions o ene gy upg ade equi emen s o enan s, bo h in e ms o likely
mone a y cos s and enancy secu i y, mus be kep in mind. Indeed, some o he
mos ulne able enan s in he PRS li e in he leas ene gy e icien p ope ies a
p esen ; Housing Assis ance Paymen (HAP) and Ren Supplemen enan s ha e
he highes sha e o p ope ies wi h a BER a ing o F o G. I is impo an ha any
policy in e en ions ake a balanced app oach o en al sec o upg ades. This
includes weighing up he need o make p og ess, bu also he dange s his p ocess
migh pose o enan s in pa icula , as i could po en ially esul in a educ ion o
In es men equi emen s o ene gy e iciency upg ades in he en al sec o |71
he en al s ock a a ime whe e he e a e al eady signi ican sho ages ela i e o
demand.

72|Re e ences
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Appendix A|75
APPENDIX A
Addi ional esul s
TABLE A.1 CONCORDANCE OF DWELLING TYPES
Census 2022
RTB da ase
SEAI BER egis e
SEAI OSS
LASHU da a
De ached house
Whole house, de ached
pa house, de ached
De ached house
House
De ached house
O he
Semi- de ached house
Whole house, de ached
pa house, de ached
Semi-de ached house
Semi-de ached/End
e ace
Te aced house
Whole house, e aced
pa house, e aced
Mid- e ace house
End o e ace house
Mid e ace
Fla o apa men , pu pose-buil
la o apa men , con e ed
Bed-si
Apa men
Fla
Maisone e, semi-de
Maisone e, e aced
Maisone e, de ached
Bed-si
Apa men
Maisone e
Top- loo apa men
Mid- loo apa men
G ound- loo ap .
Basemen dwelling
Apa men
Ap s
No es: In LASHU classi ica ion, ‘ap s’ includes apa men s and mid- e ace dwellings.
TABLE A.2 MEAN ESTIMATES OF THE RENTAL HOUSING STOCK SIZE
Es ima es wi h accoun ing o BER-exemp dwellings
Es ima es wi hou accoun ing o BER-exemp dwellings
RTB
SEAI
RTB
SEAI
BER
Es . num.
%
Es . num.
%
Es . num.
%
Es . num.
%
G
9,663
2.9
15,687
4.7
10,229
3.1
16,693
5
F
9,523
2.9
14,087
4.3
10,048
3
14,853
4.5
E2
10,239
3.1
14,897
4.5
10,808
3.3
15,732
4.8
E1
16,005
4.8
19,138
5.8
16,995
5.1
20,283
6.1
D2
31,136
9.4
36,874
11.2
32,935
10
38,930
11.8
D1
40,708
12.3
42,095
12.7
42,848
13
44,304
13.4
C3
45,029
13.6
43,022
13
47,364
14.3
45,224
13.7
C2
38,859
11.8
42,254
12.8
40,923
12.4
44,554
13.5
C1
41,306
12.5
37,019
11.2
43,725
13.2
39,277
11.9
B3
24,767
7.5
25,326
7.7
26,394
8
27,055
8.2
B2
13,054
3.9
11,678
3.5
14,068
4.3
12,573
3.8
B1
8,324
2.5
3,696
1.1
8,938
2.7
3,996
1.2
A3
8,989
2.7
4,092
1.2
9,485
2.9
4,310
1.3
A2
13,014
3.9
2,637
0.8
13,832
4.2
2,809
0.8
A1
1,922
0.6
37
0
2,040
0.6
40
0
Exemp
18,095
5.5
18,095
5.5
-
-
-
-
To al
330,632
100
330,632
100
330,632
100
330,632
100
Sub o als
F/G
19,186
6
29,774
9
20,277
6
31,546
10
E*
26,243
8
34,035
10
27,804
8
36,015
11
D*
71,844
22
78,969
24
75,783
23
83,234
25
C*
125,195
38
122,295
37
132,011
40
129,055
39
B*
46,145
14
40,699
12
49,399
15
43,624
13
A*
23,925
7
6,765
2
25,358
8
7,159
2
Exemp
18,095
5
18,095
5
-
-
-
-
To al
330,632
100
330,632
100
330,632
100
330,632
100